# US 8,787,683 B1 · Image classification > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,787,683 B1 - Document ID: US8787683B1 - Status: Granted - Kind: B1 - Application number: 13/910,493 - Filed: 2013-06-05 - Granted: 2014-07-22 - Earliest claimed: 2009-07-17 - Assignees: Google Inc. - Applicants: Google Inc. - Inventors: Yangli Hector Yee; Samy Bengio; Charles J. Rosenberg; Erik Murphy-Chutorian - Web page: https://patentgrep.com/patent/US8787683B1 ## CPC - G06F 16/5846: {using extracted text} - G06F 16/50: of still image data - G06F 16/55: Clustering; Classification - G06F 16/951: Indexing; Web crawling techniques - G06F 16/9538: Presentation of query results - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06V 30/18152: {Extracting features based on a plurality of salient regional features, e.g. "bag of words"} - G06V 30/19147: {Obtaining sets of training patterns; Bootstrap methods, e.g. bagging or boosting} - G06V 30/194: References adjustable by an adaptive method, e.g. learning - G06V 30/10: Character recognition - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract An image classification system trains an image classification model to classify images relative to text appearing with the images. Training images are iteratively selected and classified by the image classification model according to feature vectors of the training images. An independent model is trained for unique n-grams of text. The image classification system obtains text appearing with an image and parses the text into candidate labels for the image. The image classification system determines whether an image classification model has been trained for the candidate labels. When an image classification model corresponding to a candidate label has been trained, the image classification subsystem classifies the image relative to the candidate label. The image is labeled based on candidate labels for which the image is classified as a positive image. ## Claims ### Claim 1 A computer-implemented method, comprising: obtaining text associated with an image; obtaining a feature vector representing visual features of the image; parsing, in one or more processing devices, the text into candidate labels for the image, each candidate label being a unique n-gram of text; determining, in the one or more processing devices, that an image classification model has been trained for an n-gram matching one or more of the candidate labels, wherein the image classification model has been trained to classify images based on the feature vector; and assigning, in the processing device, at least one of the one or more candidate labels as a label for the image based on the feature vector and the image classification model for the n-gram matching the one or more candidate labels. ### Claim 2 (depends on 1) The method of claim 1, further comprising: computing a classification score for the image to the one or more candidate labels based on the feature vector and the image classification model for the n-gram matching the one or more candidate labels; and classifying the image as relevant to the one or more candidate labels based on the classification score. ### Claim 3 (depends on 2) The method of claim 2, wherein classifying the image to the one or more candidate labels based on the classification score comprises: determining that the image has a classification score to a particular candidate label that meets or exceeds a relevance threshold; and labeling the image with a label corresponding to the particular candidate label. ### Claim 4 (depends on 3) The method of claim 3, wherein the determining that the image has a classification score to a particular candidate label and labeling the image is performed for each of at least two candidate labels. ### Claim 5 (depends on 1) The method of claim 1, wherein obtaining text associated with the image comprises obtaining text appearing with the image on a Web page. ### Claim 6 A non-transitory computer readable medium encoded with a computer program comprising instructions that when executed cause one or more computers to perform operations comprising: obtaining text associated with an image; obtaining a feature vector representing visual features of the image; parsing the text into candidate labels for the image, each candidate label being a unique n-gram of the text; determining that an image classification model has been trained for an n-gram matching one or more of the candidate labels, wherein the image classification model has been trained to classify images based on the feature vector; and assigning at least one of the one or more candidate labels as a label for the image based on the feature vector and the image classification model for the n-gram matching the one or more candidate labels. ### Claim 7 (depends on 6) The computer readable medium of claim 6, wherein the instructions cause the one or more computers to perform operations comprising: computing a classification score for the image to the one or more candidate labels based on the feature vector and the image classification model for the n-gram matching the one or more candidate labels; and classifying the image as relevant to the one or more candidate labels based on the classification score. ### Claim 8 (depends on 7) The computer readable medium of claim 7, wherein classifying the image to the one or more candidate labels based on the classification score comprises: determining that the image has a classification score to a particular candidate label that meets or exceeds a relevance threshold; and labeling the image with a label corresponding to the particular candidate label. ### Claim 9 (depends on 8) The computer readable medium of claim 8, wherein the determining that the image has a classification score to a particular candidate label and labeling the image is performed for each of at least two candidate labels. ### Claim 10 (depends on 6) The computer readable medium of claim 6, wherein obtaining text associated with the image comprises obtaining text appearing with the image on a Web page. ### Claim 11 A system comprising: a data store storing images; and one or more computers that interact with the data store and execute instructions that cause the one more computers to perform operations comprising: obtaining text associated with an image; obtaining a feature vector representing visual features of the image; parsing the text into candidate labels for the image, each candidate label being a unique n-gram of the text; determining that an image classification model has been trained for an n-gram matching one or more of the candidate labels, wherein the image classification model has been trained to classify images based on the feature vector; and assigning at least one of the one or more candidate labels as a label for the image based on the feature vector and the image classification model for the n-gram matching the one or more candidate labels. ### Claim 12 (depends on 11) The system of claim 11, wherein the instructions cause the one or more computers to perform operations comprising: computing a classification score for the image to the one or more candidate labels based on the feature vector and the image classification model for the n-gram matching the one or more candidate labels; and classifying the image as relevant to the one or more candidate labels based on the classification score. ### Claim 13 (depends on 12) The system of claim 12, wherein classifying the image to the one or more candidate labels based on the classification score comprises: determining that the image has a classification score to a particular candidate label that meets or exceeds a relevance threshold; and labeling the image with a label corresponding to the particular candidate label. ### Claim 14 (depends on 13) The system of claim 13, wherein the determining that the image has a classification score to a particular candidate label and labeling the image is performed for each of at least two candidate labels. ### Claim 15 (depends on 11) The system of claim 11, wherein obtaining text associated with the image comprises obtaining text appearing with the image on a Web page.