Visual word proximity and linguistics for semantic video indexing and near-duplicate retrieval

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

49 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)405-414
Journal / PublicationComputer Vision and Image Understanding
Volume113
Issue number3
Publication statusPublished - Mar 2009

Abstract

Bag-of-visual-words (BoW) has recently become a popular representation to describe video and image content. Most existing approaches, nevertheless, neglect inter-word relatedness and measure similarity by bin-to-bin comparison of visual words in histograms. In this paper, we explore the linguistic and ontological aspects of visual words for video analysis. Two approaches, soft-weighting and constraint-based earth mover's distance (CEMD), are proposed to model different aspects of visual word linguistics and proximity. In soft-weighting, visual words are cleverly weighted such that the linguistic meaning of words is taken into account for bin-to-bin histogram comparison. In CEMD, a cross-bin matching algorithm is formulated such that the ground distance measure considers the linguistic similarity of words. In particular, a BoW ontology which hierarchically specifies the hyponym relationship of words is constructed to assist the reasoning. We demonstrate soft-weighting and CEMD on two tasks: video semantic indexing and near-duplicate keyframe retrieval. Experimental results indicate that soft-weighting is superior to other popular weighting schemes such as term frequency (TF) weighting in large-scale video database. In addition, CEMD shows excellent performance compared to cosine similarity in near-duplicate retrieval. © 2008 Elsevier Inc. All rights reserved.

Research Area(s)

  • CEMD matching, Linguistic similarity, Near-duplicate keyframe, Semantic concept, Soft-weighting, Visual ontology