Patent ID: 9646226
Date: 2017-05-09
CPC Classifications: G06K

Claim:
1. A method of improving precision of training data used in automated image annotation, categorization, recognition, understanding, or retrieval, comprising the steps of: providing a digital computer; receiving at the digital computer a plurality of digital images, all the images previously tagged with a target concept; executing an algorithm on the digital computer to perform the following operations: (a) extracting visual and textual features from each of the tagged images, (b) variably weighting the images based upon the extracted features, the weight of the image reflecting a relevance of the image to the concept, (c) learning a reference model for the images representing the target concept, wherein the reference model is a mixture model based on the weighted images, the mixture model being a parametric probabilistic model having mixture parameters, the weight of each image parameterized on the mixture parameters, and both the mixture parameters and the weights of the images being learned during the learning of the reference model to curb contributions of noisy images; (d) retaining images with high likelihood of correct tagging based upon the reference model, (e) iterating the steps (b) and (c) using the retained images: and using learned reference model to train an automated image annotation, categorization, recognition, understanding, or retrieval system.