# US 2019/0171910 A1 · Best Image Crop Selection > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0171910 A1 - Document ID: US20190171910A1 - Status: Published application - Kind: A1 - Application number: 15/831,638 - Filed: 2017-12-05 - Published: 2019-06-06 - Earliest claimed: 2017-12-05 - Assignees: Axis AB - Applicants: Axis AB - Inventors: Niclas Danielsson; Markus Skans - Web page: https://patentgrep.com/patent/US20190171910A1 ## CPC - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06V 10/454: {Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Methods and apparatus, including computer program products, for creating a quality annotated training data set of images for training a quality estimating neural network. A set of images depicting a same object is received. The images in the set of images have varying image quality. A probe image whose quality is to be estimated is selected from the set of images. A gallery of images is selected from the set of images. The gallery of images does not include the probe image. The probe image is compared to each image in the gallery and a match score is generated for each image comparison. Based on the match scores, a quality value is determined for the probe image. The probe image and its associated quality value are added to a quality annotated training data set for the neural network. ## Claims ### Claim 1 A method for creating a quality annotated training data set of images for training a quality estimating neural network, comprising: receiving a set of images depicting a same object, wherein the images in the set of images have varying image quality; selecting, from the set of images, a probe image whose quality is to be estimated; selecting, from the set of images, a gallery of images, wherein the gallery of images does not include the probe image; comparing the probe image to each image in the gallery and generating a match score for each image comparison; determining, based on the match scores, a quality value for the probe image; and adding the probe image and its associated quality value to a quality annotated training data set for the neural network. ### Claim 2 (depends on 1) The method of claim 1, further comprising: repeating the receiving, selecting, selecting, comparing and determining steps for each image in the set of images; and using all the probe images and their respective associated quality values as the quality annotated training data set for the neural network. ### Claim 3 (depends on 1) The method of claim 1, wherein the object is one of: a person, a vehicle, and a face. ### Claim 4 (depends on 1) The method of claim 1, wherein comparing the probe image to each image in the gallery of images includes: determining a distance between a first feature vector representing the probe image and a second feature vector representing a gallery image; and assigning a match score based on the distance between the first feature vector and the second feature vector. ### Claim 5 (depends on 4) The method of claim 4, wherein a short distance indicates a high degree of identity similarity between the probe image and the gallery image. ### Claim 6 (depends on 1) The method of claim 1, wherein the quality value for the probe image is determined as a function of a best match score for the probe image and the range of match scores for the probe image ### Claim 7 A system for creating a quality annotated training data set of images for training a quality estimating neural network, comprising: a memory; and a processor, wherein the memory contains instructions that when executed by the processor causes the processor to perform a method that includes: receiving a set of images depicting a same object, wherein the images in the set of images have varying image quality; selecting, from the set of images, a probe image whose quality is to be estimated; selecting, from the set of images, a gallery of images, wherein the gallery of images does not include the probe image; comparing the probe image to each image in the gallery and generating a match score for each image comparison; determining, based on the match scores, a quality value for the probe image; and adding the probe image and its associated quality value to a quality annotated training data set for the neural network. ### Claim 8 (depends on 7) The system of claim 7, wherein the method further comprises: repeating the receiving, selecting, selecting, comparing and determining steps for each image in the set of images; and using all the probe images and their respective associated quality values as the quality annotated training data set for the neural network. ### Claim 9 (depends on 7) The system of claim 7, wherein the object is one of: a person, a vehicle, and a face. ### Claim 10 (depends on 7) The system of claim 7, wherein comparing the probe image to each image in the gallery of images includes: determining a distance between a first feature vector representing the probe image and a second feature vector representing a gallery image; and assigning a match score based on the distance between the first feature vector and the second feature vector. ### Claim 11 (depends on 10) The system of claim 10, wherein a short distance indicates a high degree of identity similarity between the probe image and the gallery image. ### Claim 12 (depends on 7) The system of claim 7, wherein the quality value for the probe image is determined as a function of a best match score for the probe image and the range of match scores for the probe image. ### Claim 13 A computer program product for creating a quality annotated training data set of images for training a quality estimating neural network, comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instructions being executable by a processor to perform a method comprising: receiving a set of images depicting a same object, wherein the images in the set of images have varying image quality; selecting, from the set of images, a probe image whose quality is to be estimated; selecting, from the set of images, a gallery of images, wherein the gallery of images does not include the probe image; comparing the probe image to each image in the gallery and generating a match score for each image comparison; determining, based on the match scores, a quality value for the probe image; and adding the probe image and its associated quality value to a quality annotated training data set for the neural network. ### Claim 14 (depends on 13) The computer program product of claim 13, wherein the method further comprises: repeating the receiving, selecting, selecting, comparing and determining steps for each image in the set of images; and using all the probe images and their respective associated quality values as the quality annotated training data set for the neural network. ### Claim 15 (depends on 13) The computer program product of claim 13, wherein comparing the probe image to each image in the gallery of images includes: determining a distance between a first feature vector representing the probe image and a second feature vector representing a gallery image; and assigning a match score based on the distance between the first feature vector and the second feature vector.