Publication Type

Journal Article

Version

publishedVersion

Publication Date

10-2007

Abstract

This paper proposes a new approach for shot-based retrieval by optimal matching (OM), which provides an effective mechanism for the similarity measure and ranking of shots by one-to-one matching. In the proposed approach, a weighted bipartite graph is constructed to model the color similarity between two shots. Then OM based on Kuhn-Munkres algorithm is employed to compute the maximum weight of a constructed bipartite graph as the shot similarity value by one-to-one matching among frames. To improve the speed efficiency of OM, two improved algorithms are also proposed: bipartite graph construction based on subshots and bipartite graph construction based on the same number of keyframes. Besides color similarity, motion feature is also employed for shot similarity measure. A motion histogram is constructed for each shot, the motion similarity between two shots is then measured by the intersection of their motion histograms. Finally, the shot similarity is based on the linear combination of color and motion similarity. Experimental results indicate that the proposed approach achieves better performance than other methods in terms of ranking and retrieval capability.

Keywords

shot-based retrieval, OM, color and motion similarity

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

Multimedia Tools and Applications

Volume

34

Issue

2

First Page

249

Last Page

266

ISSN

1380-7501

Identifier

10.1007/s11042-006-0085-4

Publisher

Springer (part of Springer Nature): Springer Open Choice Hybrid Journals

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