US 7,333,963 B2 · Cognitive memory and auto-associative neural network based search engine for computer and network located images and photographs

A search engine for searching a computer or other information appliance, wherein said search engine seeks stored images, said stored images depicting persons' faces or objects of interest, wherein said stored images are retrieved in response to receipt of a query comprising a query image;

said query image depicting one or more persons' faces, or depicting one or more objects of interest;
wherein said search engine comprises:
(a) first means for locating images of persons' faces or objects of interest within both said query image and said stored images, said first means comprising:
i. first autoassociative neural network trained on first low resolution input patterns and first variations, wherein each of said first low resolution input patterns depicts one person's face or one object of interest, wherein each of said first low resolution input patterns and said first variations contains 2000 or fewer pixels, and wherein said first variations are created from said first low resolution input patterns by at least one of or any combination of rotation, translation, changes in scale, brightness, and contrast, and other image processing techniques;
ii. first window means for scanning over said query image and over all said stored images, creating second low resolution input patterns and second variations, said second variations generated by at least one of or any combination of rotation, translation, changes in scale, brightness, and contrast, spatial filtering, frequency filtering, spatial frequency filtering, edge detection, perspective transformation, warping, distorting, distortion correction, image to image registration, gray-level histogram modification or equalization, adjusting color characteristics, varying or adjusting color saturation, removing color, distending, compressing, squeezing, shearing, and changes in intensity;
iii. means for applying, as inputs to trained said first autoassociative neural network, said second low resolution input patterns and said second variations, seeking a low error in the difference between the input and output of said first autoassociative neural network, where said low error in difference, when below a preset threshold, indicates a detected face or object of interest;
(b) second means for interrelating said query image, depicting persons' faces or objects of interest, to stored images depicting the same persons' faces or objects of interest, said second means comprising:
second autoassociative neural network trained on first high resolution input patterns and first high resolution variations, wherein each of said first high resolution input patterns depicts one detected person's face or object of interest, said detected person's face or object of interest detected in said query image by said first means, wherein each of said first high resolution input patterns and said first high resolution variations contains 2000 or more pixels, and wherein said first high resolution variations are created from said first high resolution input patterns by at least one of or any combinations of rotation, translation, changes in scale, brightness, and contrast, spatial filtering, frequency filtering, spatial frequency filtering, edge detection, perspective transformation, warping, distorting, distortion correction, image to image registration, gray-level histogram modification or equalization, adjusting color characteristics, varying or adjusting color saturation, removing color, distending, compressing, squeezing, shearing, and changes in intensity;
ii. second window means for scanning over said stored images, creating second high resolution input patterns and second high resolution variations, said second high resolution variations generated by at least one of or any combinations of rotation, translation, changes in scale, brightness, and contrast, and other image processing techniques;
iii. means for applying, as inputs to trained said second autoassociative neural network, said second high resolution input patterns and said second high resolution variations derived from a given stored image by said second window means, seeking a second low error in the difference between the input and output of said second autoassociative neural network;
and
iv. means for identifying the given stored image that is related to said query image, wherein said given stored image is related to said query image when said second low error is below a pre-set threshold;
and
(c) third means for delivering as output response to said query image said stored images related by said second means.

11 more elements

In a computer or other information appliance, a computer-implemented method for searching and interrelating stored original images, said stored original images depicting persons' faces or objects of interest, said method comprising:

(a) assigning a unique identifier for each stored original image;
(b) locating and segmenting images of faces or objects of interest depicted in each said stored original image;
(c) creating variations of each segmented image of said faces or objects of interest by at least one of or any combinations of rotation, translation, changes in scale, brightness, and contrast, spatial filtering, frequency filtering, spatial frequency filtering, edge detection, perspective transformation, warping, distorting, distortion correction, image to image registration, gray-level histogram modification or equalization, adjusting color characteristics, varying or adjusting color saturation, removing color, distending, compressing, squeezing, shearing, and changes in intensity;
(d) creating separate folders for storage of each segmented image of a face or object of interest, each of said folders further containing said variations generated from each said segmented image of a face or object of interest, each of said folders further containing said unique identifier of said stored original image from which said segmented image of a face or object of interest was derived, wherein a folder represents any means of associating the contents thereof;
(e) training an individual autoassociative neural network for each of said folders, the training patterns for said individual autoassociative neural network comprising the images and their respective variations stored in the respective folder, thus relating each said individual autoassociative neural network to the folder containing its training patterns;
(f) storing the weights and parameters of each trained said individual autoassociative neural network;
(g) applying input patterns to each trained autoassociative neural network, said input patterns comprising all images stored in all said folders;
(h) determining the error, the difference between a given input pattern and the corresponding output pattern of a particular trained autoassociative neural network;
(i) adding all unique identifiers stored in the folder of said given input pattern to the list of unique identifiers stored in the folder related to said particular trained autoassociative neural network, if said error is below a pre-set threshold;
and
(j) performing steps (h) and (i) for all pairings of input patterns and trained autoassociative neural networks;
whereby each of said folders contains a unique identifier or a set of unique identifiers, thereby connecting the images of faces or objects of interest in each folder to a stored original image or to a set of stored original images, and thereby interrelating stored original images that depict faces or objects of interest in common.

9 more elements

In a computer or other information appliance, a computer-implemented method for searching and interrelating stored original images, said stored original images depicting persons' faces or objects of interest, said method comprising:

(a) locating each stored original image;
(b) locating and segmenting images of faces or objects of interest depicted in each said stored original image;
(c) creating variations of each segmented image of said faces or objects of interest by at least one of or any combinations of rotation, translation, changes in scale, brightness, and contrast, spatial filtering, frequency filtering, spatial frequency filtering, edge detection, perspective transformation, warping, distorting, distortion correction, image to image registration, gray-level histogram modification or equalization, adjusting color characteristics, varying or adjusting color saturation, removing color, distending, compressing, squeezing, shearing, and changes in intensity;
(d) creating separate folders for storage of each image of a segmented face or object of interest, each of said folders further containing said variations generated from each said segmented image of a face or object of interest, wherein a folder represents any means of associating the contents thereof;
(e) training an individual autoassociative neural network for each of said folders, the training patterns for said individual autoassociative neural network comprising said segmented images of faces or objects of interest, and further comprising their respective said variations stored in the same folder;
(f) storing the weights and parameters of said individual autoassociative neural network;
(g) applying input patterns to each trained autoassociative neural network, said input patterns comprising all images stored in all said folders;
(h) grouping together the folder containing a given input pattern and the folder associated with a particular trained autoassociative neural network, whenever the error is below a pre-set threshold, wherein said error is the difference between said given input pattern and said particular trained autoassociative neural network output pattern;
(i) performing said grouping step for each folder and establishing all possible such groups, each such group containing all of the folders containing images of a given face or object of interest;
(j) attaching the name or keywords to a group if one or more of the folders of the group contain images that were derived from a stored original image in which segmented faces or objects of interest had been identified by said name or keywords;
and
(k) attaching the name or keywords to a group if one or more of the images stored in the folders of the group were manually identified by said name or keywords.

9 more elements

In a computer or other information appliance, a computer-implemented method for searching and interrelating original images, said original images depicting persons' faces or objects of interest, wherein said original images are stored on web servers or other information storage appliances, wherein said original images are accessible to said computer or other information appliance, said method comprising:

(a) crawling or searching a computer network, the World Wide Web, or other types of interconnected networks, to collect original images and their universal resource locators, URLs, said URLs indicating the locations of said collected original images;
(b) locating and segmenting images of faces or objects of interest depicted in collected original images;
(c) creating variations of each segmented image of said faces or objects of interest by at least one of or any combinations of rotation, translation, changes in scale, brightness, and contrast, spatial filtering, frequency filtering, spatial frequency filtering, edge detection, perspective transformation, warping, distorting, distortion correction, image to image registration, gray-level histogram modification or equalization, adjusting color characteristics, varying or adjusting color saturation, removing color, distending, compressing, squeezing, shearing, and changes in intensity;
(d) creating a single folder for storage of said each segmented image of a face or an object of interest, each said single folder further containing said variations generated from each said segmented image of a face or an object of interest, each said single folder further containing the URL of said collected original image from which said segmented image of a face or object of interest was derived, wherein a folder represents any means of associating the contents thereof;
(e) applying input patterns to an autoassociative neural network, said input patterns comprising the images contained within said single folder;
(f) determining the error, wherein said error is the difference between one of said input patterns from one said single folder and the corresponding output pattern from said autoassociative neural network;
(g) training said autoassociative neural network with the patterns contained in said single folder, if said error is above a pre-set threshold for every pattern in said single folder;
(h) performing the following steps for a hit pattern in said single folder, wherein said hit pattern is a pattern for which said error is below said pre-set threshold:
i. comparing said hit pattern to all patterns in all other folders;
and
ii. adding the hit URL to the folder containing the matching pattern, wherein said matching pattern is the pattern having the smallest difference between said matching pattern and said hit pattern, wherein said hit URL is the URL in the folder containing said hit pattern;
and
(i) performing steps (d), (e), (f), (g), and (h) for each said segmented image;
whereby each folder contains a URL or a set of URLs, thereby connecting the images of faces or objects of interest in each folder to an original image or to a set of original images, thus interrelating original images depicting faces or objects of interest that are in common.

11 more elements

In a computer or other information appliance, a computer-implemented method for searching and interrelating original images, said original images depicting persons faces or objects of interest, wherein said original images are stored on web servers or other information storage appliances, wherein said original images are accessible to said computer or other information appliance, said method comprising:

(a) crawling or searching a computer network, the World Wide Web, or other types of interconnected networks, to collect original images and their universal resource locators, URLs, said URLs indicating the locations of said collected original images;
(b) locating and segmenting images of faces or objects of interest depicted in collected original images;
(c) creating variations of each segmented image of said faces or objects of interest by at least one of or any combinations of rotation, translation, changes in scale, brightness, and contrast, spatial filtering, frequency filtering, spatial frequency filtering, edge detection, perspective transformation, warping, distorting, distortion correction, image to image registration, gray-level histogram modification or equalization, adjusting color characteristics, varying or adjusting color saturation, removing color, distending, compressing, squeezing, shearing, and changes in intensity;
(d) creating a single folder for storage of said each segmented image of a face or an object of interest, each said single folder further containing said variations generated from each said segmented image of a face or an object of interest, each said single folder further containing the URL of said collected original image from which said segmented image of a face or object of interest was derived, wherein a folder represents any means of associating the contents thereof;
(e) applying input patterns to each autoassociative neural network of a plurality of autoassociative neural networks, said input patterns comprising the images contained within said single folder;
(f) determining the errors, wherein said errors are the differences between one of said input patterns from one said single folder and the corresponding output pattern from said plurality of autoassociative neural networks;
(g) training one of said autoassociative neural networks with the patterns contained in said single folder thereby associating said single folder with said one of said autoassociative neural networks, if all said errors are above a pre-set threshold for every pattern in said single folder;
(h) performing the following steps for a hit pattern in said single folder, wherein said hit pattern is a pattern for which said error is below said pre-set threshold:
i. comparing said hit pattern to all patterns in all folders associated with any of said autoassociative neural networks whose error, when presented with said hit pattern, is below said pre-set threshold;
and
ii. adding the hit URL to the folder containing the matching pattern, wherein said matching pattern is the pattern having the smallest difference between said matching pattern and said hit pattern, wherein said hit URL is the URL in the folder containing said hit pattern;
and
(i) performing steps (d), (e), (f), (g), and (h) for each said segmented image;
whereby each folder contains a URL or a set of URLs, thereby connecting the images of faces or objects of interest in each folder to an original image or to a set of original images, thus interrelating original images depicting faces or objects of interest that are in common.

11 more elements

A computer program product for use with a computer or information appliance comprising:

a computer operable medium having computer readable code, the computer readable code being effective to perform a computer-implemented method for searching and interrelating stored original images, said stored original images depicting persons' faces or objects of interest, said computer-implemented method comprising:
(a) assigning a unique identifier for each stored original image;
(b) locating and segmenting images of faces or objects of interest depicted in each said stored original image;
(c) creating variations of each segmented image of said faces or objects of interest by at least one of or any combinations of rotation, translation, changes in scale, brightness, and contrast, spatial filtering, frequency filtering, spatial frequency filtering, edge detection, perspective transformation, warping, distorting, distortion correction, image to image registration, gray-level histogram modification or equalization, adjusting color characteristics, varying or adjusting color saturation, removing color, distending, compressing, squeezing, shearing, and changes in intensity;
(d) creating separate folders for storage of each segmented image of a face or object of interest, each of said folders further containing said variations generated from each said segmented image of a face or object of interest, each of said folders further containing said unique identifier of said stored original image from which said segmented image of a face or object of interest was derived, wherein a folder represents any means of associating the contents thereof;
(e) training an individual autoassociative neural network for each of said folders, the training patterns for said individual autoassociative neural network comprising the images and their respective variations stored in the respective folder, thus relating each said individual autoassociative neural network to the folder containing its training patterns;
(f) storing the weights and parameters of each trained said individual autoassociative neural network;
(g) applying input patterns to each trained autoassociative neural network, said input patterns comprising all images stored in all said folders;
(h) determining the error, the difference between a given input pattern and the corresponding output pattern of a particular trained autoassociative neural network;
(i) adding all unique identifiers stored in the folder of said given input pattern to the list of unique identifiers stored in the folder related to said particular trained autoassociative neural network, if said error is below a pre-set threshold;
and
(j) performing steps (h) and (i) for all pairings of input patterns and trained autoassociative neural networks;
whereby each of said folders contains a unique identifier or a set of unique identifiers, thereby connecting the images of faces or objects of interest in each folder to a stored original image or to a set of stored original images, and thereby interrelating stored original images that depict faces or objects of interest in common.

10 more elements

A computer system for searching and interrelating stored images, said stored images depicting persons' faces or objects of interest, said computer system comprising:

(a) means for assigning a unique identifier for each stored original image;
(b) means for locating and segmenting images of faces or objects of interest depicted in each said stored original image;
(c) means for creating variations of each segmented image of said faces or objects of interest by at least one of or any combinations of rotation, translation, changes in scale, brightness, and contrast, spatial filtering, frequency filtering, spatial frequency filtering, edge detection, perspective transformation, warping, distorting, distortion correction, image to image registration, gray-level histogram modification or equalization, adjusting color characteristics, varying or adjusting color saturation, removing color, distending, compressing, squeezing, shearing, and changes in intensity;
(d) means for creating separate folders for storage of each segmented image of a face or object of interest, each of said folders further containing said variations generated from each said segmented image of a face or object of interest, each of said folders further containing said unique identifier of said stored original image from which said segmented image of a face or object of interest was derived, wherein a folder represents any means of associating the contents thereof;
(e) means for training an individual autoassociative neural network for each of said folders, the training patterns for said individual autoassociative neural network comprising the images and their respective variations stored in the respective folder, thus relating each said individual autoassociative neural network to the folder containing its training patterns;
(f) means for storing the weights and parameters of each trained said individual autoassociative neural network;
(g) means for applying input patterns to each trained autoassociative neural network, said input patterns comprising all images stored in all said folders;
(h) means for determining the error, the difference between a given input pattern and the corresponding output pattern of a particular trained autoassociative neural network;
(i) means for adding all unique identifiers stored in the folder of said given input pattern to the list of unique identifiers stored in the folder related to said particular trained autoassociative neural network, if said error is below a pre-set threshold;
and
(j) means for performing steps (h) and (i) for all pairings of input patterns and trained autoassociative neural networks;
whereby each of said folders contains a unique identifier or a set of unique identifiers, thereby connecting the images of faces or objects of interest in each folder to a stored original image or to a set of stored original images, and thereby interrelating stored original images that depict faces or objects of interest in common.

9 more elements

A computer program product for use with a computer or information appliance comprising:

a computer operable medium having computer readable code, the computer readable code being effective to perform a computer-implemented method for searching and interrelating stored original images, said stored original images depicting persons faces or objects of interest, said method comprising:
(a) locating each stored original image;
(b) locating and segmenting images of faces or objects of interest depicted in each said stored original image;
(c) creating variations of each segmented image of said faces or objects of interest by at least one of or any combinations of rotation, translation, changes in scale, brightness, and contrast, spatial filtering, frequency filtering, spatial frequency filtering, edge detection, perspective transformation, warping, distorting, distortion correction, image to image registration, gray-level histogram modification or equalization, adjusting color characteristics, varying or adjusting color saturation, removing color, distending, compressing, squeezing, shearing, and changes in intensity;
(d) creating separate folders for storage of each image of a segmented face or object of interest, each of said folders further containing said variations generated from each said segmented image of a face or object of interest, wherein a folder represents any means of associating the contents thereof;
(e) training an individual autoassociative neural network for each of said folders, the training patterns for said individual autoassociative neural network comprising said segmented images of faces or objects of interest, and further comprising their respective said variations stored in the same folder;
(f) storing the weights and parameters of said individual autoassociative neural network;
(g) applying input patterns to each trained autoassociative neural network, said input patterns comprising all images stored in all said folders;
(h) grouping together the folder containing a given input pattern and the folder associated with a particular trained autoassociative neural network, whenever the error is below a pre-set threshold, wherein said error is the difference between said given input pattern and said particular trained autoassociative neural network output pattern;
(i) performing said grouping step for each folder and establishing all possible such groups, each such group containing all of the folders containing images of a given face or object of interest;
(j) attaching the name or keywords to a group if one or more of the folders of the group contain images that were derived from a stored original image in which segmented faces or objects of interest had been identified by said name or keywords;
and
(k) attaching the name or keywords to a group if one or more of the images stored in the folders of the group were manually identified by said name or keywords.

10 more elements

A computer system for searching and interrelating stored original images, said stored original images depicting persons faces or objects of interest, said system comprising:

(a) means for locating each stored original image;
(b) means for locating and segmenting images of faces or objects of interest depicted in each said stored original image;
(c) means for creating variations of each segmented image of said faces or objects of interest by at least one of or any combinations of rotation, translation, changes in scale, brightness, and contrast, spatial filtering, frequency filtering, spatial frequency filtering, edge detection, perspective transformation, warping, distorting, distortion correction, image to image registration, gray-level histogram modification or equalization, adjusting color characteristics, varying or adjusting color saturation, removing color, distending, compressing, squeezing, shearing, and changes in intensity;
(d) means for creating separate folders for storage of each image of a segmented face or object of interest, each of said folders further containing said variations generated from each said segmented image of a face or object of interest, wherein a folder represents any means of associating the contents thereof;
(e) means for training an individual autoassociative neural network for each of said folders, the training patterns for said individual autoassociative neural network comprising said segmented images of faces or objects of interest, and further comprising their respective said variations stored in the same folder;
(f) means for storing the weights and parameters of said individual autoassociative neural network;
(g) means for applying input patterns to each trained autoassociative neural network, said input patterns comprising all images stored in all said folders;
(h) means for grouping together the folder containing a given input pattern and the folder associated with a particular trained autoassociative neural network, whenever the error is below a pre-set threshold, wherein said error is the difference between said given input pattern and said particular trained autoassociative neural network output pattern;
(i) means for performing said grouping step for each folder and establishing all possible such groups, each such group containing all of the folders containing images of a given face or object of interest;
(j) means for attaching the name or keywords to a group if one or more of the folders of the group contain images that were derived from a stored original image in which segmented faces or objects of interest had been identified by said name or keywords;
and
(k) means for attaching the name or keywords to a group if one or more of the images stored in the folders of the group were manually identified by said name or keywords.

9 more elements

A computer program product for use with a computer or information appliance comprising:

a computer operable medium having computer readable code, the computer readable code being effective to perform a computer-implemented method for searching and interrelating original images, said original images depicting persons' faces or objects of interest, wherein said original images are stored on web servers or other information storage appliances, wherein said original images are accessible to said computer or other information appliance, said method comprising:
(a) crawling or searching a computer network, the World Wide Web, or other types of interconnected networks, to collect original images and their universal resource locators, URLs, said URLs indicating the locations of said collected original images;
(b) locating and segmenting images of faces or objects of interest depicted in collected original images;
(c) creating variations of each segmented image of said faces or objects of interest by at least one of or any combinations of rotation, translation, changes in scale, brightness, and contrast, spatial filtering, frequency filtering, spatial frequency filtering, edge detection, perspective transformation, warping, distorting, distortion correction, image to image registration, gray-level histogram modification or equalization, adjusting color characteristics, varying or adjusting color saturation, removing color, distending, compressing, squeezing, shearing, and changes in intensity;
(d) creating a single folder for storage of said each segmented image of a face or an object of interest, each said single folder further containing said variations generated from each said segmented image of a face or an object of interest, each said single folder further containing the URL of said collected original image from which said segmented image of a face or object of interest was derived, wherein a folder represents any means of associating the contents thereof;
(e) applying input patterns to an autoassociative neural network, said input patterns comprising the images contained within said single folder;
(f) determining the error, wherein said error is the difference between one of said input patterns from one said single folder and the corresponding output pattern from said autoassociative neural network;
(g) training said autoassociative neural network with the patterns contained in said single folder, if said error is above a pre-set threshold for every pattern in said single folder;
(h) performing the following steps for a hit pattern in said single folder, wherein said hit pattern is a pattern for which said error is below said pre-set threshold:
i. comparing said hit pattern to all patterns in all other folders;
and
ii. adding the hit URL to the folder containing the matching pattern, wherein said matching pattern is the pattern having the smallest difference between said matching pattern and said hit pattern, wherein said hit URL is the URL in the folder containing said hit pattern;
and
(i) performing steps (d), (e), (f), (g), and (h) for each said segmented image;
whereby each folder contains a URL or a set of URLs, thereby connecting the images of faces or objects of interest in each folder to an original image or to a set of original images, thus interrelating original images depicting faces or objects of interest that are in common.

12 more elements

A computer system for searching and interrelating original images depicting persons' faces or objects of interest, wherein said original images are stored on external web servers or other information storage appliances, and wherein said original images are accessible to said computer system, said system comprising:

(a) means for crawling or searching a computer network, the World Wide Web, or other types of interconnected networks, to collect original images and their universal resource locators, URLs, said URLs indicating the locations of said collected original images;
(b) means for locating and segmenting images of faces or objects of interest depicted in collected original images;
(c) means for creating variations of each segmented image of said faces or objects of interest by at least one of or any combinations of rotation, translation, changes in scale, brightness, and contrast, spatial filtering, frequency filtering, spatial frequency filtering, edge detection, perspective transformation, warping, distorting, distortion correction, image to image registration, gray-level histogram modification or equalization, adjusting color characteristics, varying or adjusting color saturation, removing color, distending, compressing, squeezing, shearing, and changes in intensity;
(d) means for creating a single folder for storage of said each segmented image of a face or an object of interest, each said single folder further containing said variations generated from each said segmented image of a face or an object of interest, each said single folder further containing the URL of said collected original image from which said segmented image of a face or object of interest was derived, wherein a folder represents any means of associating the contents thereof;
(e) means for applying input patterns to an autoassociative neural network, said input patterns comprising the images contained within said single folder;
(f) means for determining the error, wherein said error is the difference between one of said input patterns from one said single folder and the corresponding output pattern from said autoassociative neural network;
(g) means for training said autoassociative neural network with the patterns contained in said single folder, if said error is above a pre-set threshold for every pattern in said single folder;
(h) means for performing the following steps for a hit pattern in said single folder, wherein said hit pattern is a pattern for which said error is below said pre-set threshold:
i. comparing said hit pattern to all patterns in all other folders;
and
ii. adding the hit URL to the folder containing the matching pattern, wherein said matching pattern is the pattern having the smallest difference between said matching pattern and said hit pattern, wherein said hit URL is the URL in the folder containing said hit pattern;
and
(i) means for performing steps (d), (e), (f), (g), and (h) for each said segmented image;
whereby each folder contains a URL or a set of URLs, thereby connecting the images of faces or objects of interest in each folder to an original image or to a set of original images, thus interrelating original images depicting faces or objects of interest that are in common.

11 more elements

A computer program product for use with a computer or information appliance comprising:

a computer operable medium having computer readable code, the computer readable code being effective to perform a computer-implemented method for searching and interrelating original images, said original images depicting persons' faces or objects of interest, wherein said original images are stored on web servers or other information storage appliances, wherein said original images are accessible to said computer or other information appliance, said method comprising:
(a) crawling or searching a computer network, the World Wide Web, or other types of interconnected networks, to collect original images and their universal resource locators, URLs, said URLs indicating the locations of said collected original images;
(b) locating and segmenting images of faces or objects of interest depicted in collected original images;
(c) creating variations of each segmented image of said faces or objects of interest by at least one of or any combinations of rotation, translation, changes in scale, brightness, and contrast, spatial filtering, frequency filtering, spatial frequency filtering, edge detection, perspective transformation, warping, distorting, distortion correction, image to image registration, gray-level histogram modification or equalization, adjusting color characteristics, varying or adjusting color saturation, removing color, distending, compressing, squeezing, shearing, and changes in intensity;
(d) creating a single folder for storage of said each segmented image of a face or an object of interest, each said single folder further containing said variations generated from each said segmented image of a face or an object of interest, each said single folder further containing the URL of said collected original image from which said segmented image of a face or object of interest was derived, wherein a folder represents any means of associating the contents thereof;
(e) applying input patterns to each autoassociative neural network of a plurality of autoassociative neural networks, said input patterns comprising the images contained within said single folder;
(f) determining the errors, wherein said errors are the differences between one of said input patterns from one said single folder and the corresponding output pattern from said plurality of autoassociative neural networks;
(g) training one of said autoassociative neural networks with the patterns contained in said single folder thereby associating said single folder with said one of said autoassociative neural networks, if all said errors are above a pre-set threshold for every pattern in said single folder;
(h) performing the following steps for a hit pattern in said single folder, wherein said hit pattern is a pattern for which said error is below said pre-set threshold:
i. comparing said hit pattern to all patterns in all folders associated with any of said autoassociative neural networks whose error, when presented with said hit pattern, is below said pre-set threshold;
and
ii. adding the hit URL to the folder containing the matching pattern, wherein said matching pattern is the pattern having the smallest difference between said matching pattern and said hit pattern, wherein said hit URL is the URL in the folder containing said hit pattern;
and
(i) performing steps (d), (e), (f), (g), and (h) for each said segmented image;
whereby each folder contains a URL or a set of URLs, thereby connecting the images of faces or objects of interest in each folder to an original image or to a set of original images, thus interrelating original images depicting faces or objects of interest that are in common.

12 more elements

A computer system for searching and interrelating original images depicting faces or objects of interest, wherein said original images are stored on web servers or other information storage appliances, and said original images are accessible to said computer system, said system further comprising:

(a) means for crawling or searching a computer network, the World Wide Web, or other types of interconnected networks, to collect original images and their universal resource locators, URLs, said URLs indicating the locations of said collected original images;
(b) means for locating and segmenting images of faces or objects of interest depicted in collected original images;
(c) means for creating variations of each segmented image of said faces or objects of interest by at least one of or any combinations of rotation, translation, changes in scale, brightness, and contrast, spatial filtering, frequency filtering, spatial frequency filtering, edge detection, perspective transformation, warping, distorting, distortion correction, image to image registration, gray-level histogram modification or equalization, adjusting color characteristics, varying or adjusting color saturation, removing color, distending, compressing, squeezing, shearing, and changes in intensity;
(d) means for creating a single folder for storage of said each segmented image of a face or an object of interest, each said single folder further containing said variations generated from each said segmented image of a face or an object of interest, each said single folder further containing the URL of said collected original image from which said segmented image of a face or object of interest was derived, wherein a folder represents any means of associating the contents thereof;
(e) means for applying input patterns to each autoassociative neural network of a plurality of autoassociative neural networks, said input patterns comprising the images contained within said single folder;
(f) means for determining the errors, wherein said errors are the differences between one of said input patterns from one said single folder and the corresponding output pattern from said plurality of autoassociative neural networks;
(g) means for training one of said autoassociative neural networks with the patterns contained in said single folder thereby associating said single folder with said one of said autoassociative neural networks, if all said errors are above a pre-set threshold for every pattern in said single folder;
(h) means for performing the following steps for a hit pattern in said single folder, wherein said hit pattern is a pattern for which said error is below said pre-set threshold:
i. comparing said hit pattern to all patterns in all folders associated with any of said autoassociative neural networks whose error, when presented with said hit pattern, is below said pre-set threshold;
and
ii. adding the hit URL to the folder containing the matching pattern, wherein said matching pattern is the pattern having the smallest difference between said matching pattern and said hit pattern, wherein said hit URL is the URL in the folder containing said hit pattern;
and
(i) means for performing steps (d), (e), (f), (g), and (h) for each said segmented image;
whereby each folder contains a URL or a set of URLs, thereby connecting the images of faces or objects of interest in each folder to an original image or to a set of original images, thus interrelating original images depicting faces or objects of interest that are in common.

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Cognitive memory and auto-associative neural network based search engine for computer and network located images and photographs

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Cognitive memory and auto-associative neural network based search engine for computer and network located images and photographs

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Abstract

Claims20