Columbia Consumer Video (CCV) Database
--- A Benchmark for Consumer Video Analysis
Recognizing visual content in unconstrained videos has become a very important problem for many applications. Existing corpora for video analysis lack scale and/or content diversity, and thus limited the needed progress in this critical area. To stimulate innovative research on this challenging issue, we constructed a new database called CCV, containing 9,317 YouTube videos over 20 semantic categories. The database was collected with extra care to ensure relevance to consumer's interest and originality of video content without post-editing. Such videos typically have very little textual annotation and thus can benefit from the development of automatic content analysis techniques.