Learning category classifiers for a video corpus

A non-transitory computer-readable storage medium having executable computer program instructions embodied therein for determining category classifiers applicable to digital media items of a digital media repository, actions of the computer program instructions 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 media item concept classifiers derived from the media items and associated with concepts derived from textual metadata of the digital media items in the digital media repository;
• computing consistency scores for a plurality of the categories based at least in part on scores obtained from media item concept classifiers associated with concepts corresponding to the instances of the plurality of categories;
• selectively removing the categories of the category-instance repository based at least in part 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 media item concept classifiers of concepts associated with the category, the determined category classifier when applied to a media item producing a score indicating whether the media item represents the category for which the category classifier was determined.

A computer system for determining category classifiers applicable to videos of a digital video repository, the system comprising:

• a computer processor;
• and
• a computer program executable by the computer processor and performing actions comprising:
• creating a category-instance repository comprising relationships between categories and instances of categories, the creating comprising:
• applying textual patterns over a corpus of documents comprising textual portions, and
• computing strengths for category-instance relationships based at least in part on frequencies of co-occurrence of the categories and instances;
• training a set of video concept classifiers on the videos and on textual metadata associated with the videos, each of a plurality of the classifiers corresponding to a concept derived from the textual metadata;
• removing instances of categories, responsive to the instances not corresponding to any of the concepts;
• filtering the categories of the category-instance repository based on the concepts associated with the video concept classifiers to remove categories that are not likely to be accurately recognized in videos;
• removing inconsistent categories by:
• identifying, as video concept classifiers associated with a category, video concept classifiers of concepts with labels corresponding to labels of the instances of the category;
• applying the video concept classifiers associated with the category to a video, thereby obtaining concept scores;
• computing a consistency score for the category based at least in part on the concept scores;
• and
• removing the category responsive to the computed consistency score indicating some threshold level of inconsistency;
• and
• determining, for each of a plurality of the categories, a category classifier based at least in part on the video concept classifiers of concepts associated with the category.

A computer-implemented 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 media item concept classifiers derived from media items and associated with concepts derived from textual metadata of the media items;
• computing consistency scores for a plurality of the categories based at least in part on scores obtained from media item concept classifiers associated with concepts corresponding to the instances of the plurality of categories;
• selectively removing the categories of the category-instance repository based at least in part 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 media item concept classifiers associated with concepts that are associated with the category, the determined category classifier when applied to a media item producing a score indicating whether the media item represents the category for which the category classifier was determined.