Patent Document ID: 8819024
Application ID: 12950955

Base Claim:
1. A computer-implemented method for determining category classifiers applicable to videos of a digital video repository, the method comprising: accessing a category-instance repository comprising relationships between categories and instances of categories, the category-instance repository derived from a corpus of documents comprising textual portions, the derivation comprising computing strengths for relationships between categories and instances based at least in part on frequencies of co-occurrence of the categories and instances over the corpus of documents; accessing a set of video concept classifiers derived from the videos and associated with concepts derived from textual metadata of the videos of the digital video repository; computing consistency scores for a plurality of the categories based at least in part on scores obtained from video concept classifiers associated with concepts corresponding to the instances of the plurality of categories; selectively removing categories of the category-instance repository based at least in part on whether the computed consistency scores indicate a threshold level of inconsistency; and determining, for each category of a plurality of the categories not removed, a category classifier based at least in part on the video concept classifiers of concepts associated with the category, the determined category classifier when applied to a video producing a score indicating whether the video represents the category for which the category classifier was determined.

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Claim 8:
8. The computer-implemented method of claim 1 , wherein the accessing the set of video concept classifiers comprises: storing a set of concepts derived from textual metadata of the videos; initializing a set of candidate classifiers, each candidate classifier associated with one of the concepts corresponding to the video concept classifiers; extracting features from the videos, including a set of training features from a training set of the videos and a set of validation features from a validation set of the videos; iteratively learning accurate classifiers by iteratively performing the steps of: training the candidate classifiers based at least in part on the set of training features; determining which of the trained candidate classifiers accurately classify videos, based at least in part on application of the trained candidate classifiers to the set of validation features; applying the candidate classifiers determined to be accurate to ones of the features, thereby obtaining a set of scores, and adding the set of scores to the set of training features; and storing, as the video concept classifiers, the candidate classifiers determined to be accurate.