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US 2011/0085728 A1Published application
Detecting near duplicate images
OwnerYuli Gao, Feng Tanginventors
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Abstract
Near duplicate images are detected based on local structure feature matching of local features that are extracted from the images. The matching process also may involve detecting near duplicate images based on metadata features and global image features. A computation-sensitive cascaded classifier may be used together with an on-demand feature extraction to detect near duplicate images with improved efficiency and reduced computational cost.
Claims20
A method, comprising:
- extracting a first set of local features from a first image and a second set of the local features from a second image;
- determining one or more candidate matches of the local features in the first set and in the second set;
- for each of the candidate matches,
- selecting a first group of a specified number of nearest neighbor ones of the local features that are nearest to the local feature of the candidate match in the first image,
- choosing a second group of the specified number of nearest neighbor ones of the local features that are nearest to the local feature of the candidate match in the second image,
- ascertaining matches between ones of the neighbor local features in the first group and corresponding ones of the nearest neighbor local features in the second group, and
- designating the candidate match as either a true match or a non-match based on the ascertained matches between nearest neighbor local features;
- and
- classifying the first and second images as either near duplicate images or non-near duplicate images based on the true matches.
6 more elements
Apparatus, comprising:
- a computer-readable medium storing computer-readable instructions;
- and
- a data processor coupled to the computer-readable medium, operable to execute the instructions, and based at least in part on the execution of the instructions operable to perform operations comprising
- extracting a first set of local features from a first image and a second set of the local features from a second image;
- determining one or more candidate matches of the local features in the first set and in the second set;
- for each of the candidate matches,
- selecting a first group of a specified number of nearest neighbor ones of the local features that are nearest to the local feature of the candidate match in the first image,
- choosing a second group of the specified number of nearest neighbor ones of the local features that are nearest to the local feature of the candidate match in the second image,
- ascertaining matches between ones of the neighbor local features in the first group and corresponding ones of the nearest neighbor local features in the second group, and
- designating the candidate match as either a true match or a non-match based on the ascertained matches between nearest neighbor local features;
- and
- classifying the first and second images as either near duplicate images or non-near duplicate images based on the true matches.
9 more elements
At least one computer-readable medium having computer-readable program code embodied therein, the computer-readable program code adapted to be executed by a computer to implement a method comprising:
- extracting a first set of local features from a first image and a second set of the local features from a second image;
- determining one or more candidate matches of the local features in the first set and in the second set;
- for each of the candidate matches,
- selecting a first group of a specified number of nearest neighbor ones of the local features that are nearest to the local feature of the candidate match in the first image,
- choosing a second group of the specified number of nearest neighbor ones of the local features that are nearest to the local feature of the candidate match in the second image,
- ascertaining matches between ones of the neighbor local features in the first group and corresponding ones of the nearest neighbor local features in the second group, and
- designating the candidate match as either a true match or a non-match based on the ascertained matches between nearest neighbor local features;
- and
- classifying the first and second images as either near duplicate images or non-near duplicate images based on the true matches.
6 more elements
A method, comprising:
- extracting features in a current feature set from a first image and a second image, wherein the current feature set is in a sequence of successive feature sets that consist of respective sets of constituent features and are arranged in order of increasing computational cost associated with extraction of their respective constituent features;
- classifying the first image and the second image as either near duplicate images or candidate non-near duplicate images based on the extracted features in the current feature set;
- in response to each classification of the first image and the second image as candidate non-near duplicate images based on the extracted features of the current feature set, repeating the extracting, the classifying, and the repeating with the next successive one of the feature sets following the current feature set in the sequence as the current feature set.
Apparatus, comprising:
- a computer-readable medium storing computer-readable instructions;
- and
- a data processor coupled to the computer-readable medium, operable to execute the instructions, and based at least in part on the execution of the instructions operable to perform operations comprising
- extracting features in a current feature set from a first image and a second image, wherein the current feature set is in a sequence of successive feature sets that consist of respective sets of constituent features and are arranged in order of increasing computational cost associated with extraction of their respective constituent features;
- classifying the first image and the second image as either near duplicate images or candidate non-near duplicate images based on the extracted features in the current feature set;
- in response to each classification of the first image and the second image as candidate non-near duplicate images based on the extracted features of the current feature set, repeating the extracting, the classifying, and the repeating with the next successive one of the feature sets following the current feature set in the sequence as the current feature set.
3 more elements
At least one computer-readable medium having computer-readable program code embodied therein, the computer-readable program code adapted to be executed by a computer to implement a method comprising:
- extracting features in a current feature set from a first image and a second image, wherein the current feature set is in a sequence of successive feature sets that consist of respective sets of constituent features and are arranged in order of increasing computational cost associated with extraction of their respective constituent features;
- classifying the first image and the second image as either near duplicate images or candidate non-near duplicate images based on the extracted features in the current feature set;
- in response to each classification of the first image and the second image as candidate non-near duplicate images based on the extracted features of the current feature set, repeating the extracting, the classifying, and the repeating with the next successive one of the feature sets following the current feature set in the sequence as the current feature set.
A method, comprising:
- determining a sequence of successive feature sets that consist of respective sets of constituent features and are arranged in order of increasing computational cost associated with extraction of their respective constituent features;
- building a cascade of successive classification stages, wherein the building comprises training each of the classification stages on a respective one of the feature sets such that the classification stage is operable to classify images as either near duplicate images or candidate non-near duplicate images based on the features of the respective feature set that are extracted from the images, wherein the classification stages are arranged successively in the order of the successive feature sets in the sequence.
Dependencies are inferred from the claim text.