Xie, Lexing; Yan, Rong; Tesic, Jelena; Natsev, Apostol; Smith, John R
This paper presents probabilistic visual concept trees, a model for large visual semantic taxonomy structures and its use in visual concept detection. Organizing visual semantic knowledge systematically is one of the key challenges towards large-scale concept detection, and one that is complementary to optimizing visual classification for individual concepts. Semantic concepts have traditionally been treated as isolated nodes, a densely-connected web, or a tree. Our analysis shows that none of...[Show more]
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