Publication Type

Journal Article

Version

publishedVersion

Publication Date

3-2009

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.

Keywords

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

Discipline

Data Storage Systems | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

Computer Vision and Image Understanding

Volume

113

Issue

3

First Page

405

Last Page

414

ISSN

1077-3142

Identifier

10.1016/j.cviu.2008.10.002

Publisher

Elsevier

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