Bag-of-visual-words expansion using visual relatedness for video indexing | Zendy

YuGang Jiang | Zendy; ChongWah Ngo | Zendy

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Bag-of-visual-words expansion using visual relatedness for video indexing

Author(s) -

YuGang Jiang,

ChongWah Ngo

Publication year - 2008

Publication title -

singapore management university institutional knowledge (ink) (singapore management university)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.1145/1390334.1390495

Subject(s) - computer science , search engine indexing , visual word , bag of words model , benchmark (surveying) , artificial intelligence , word (group theory) , bag of words model in computer vision , visualization , pattern recognition (psychology) , information retrieval , image retrieval , computer vision , natural language processing , image (mathematics) , mathematics , geometry , geodesy , geography

Bag-of-visual-words (BoW) has been popular for visual classification in recent years. In this paper, we propose a novel BoW expansion method to alleviate the effect of visual word correlation problem. We achieve this by diffusing the weights of visual words in BoW based on visual word relatedness, which is rigorously defined within a visual ontology. The proposed method is tested in video indexing experiment on TRECVID-2006 video retrieval benchmark, and an improvement of 7% over the traditional BoW is reported.

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