Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2008

Abstract

Near-duplicate image retrieval plays an important role in many real-world multimedia applications. Most previous approaches have some limitations. For example, conventional appearance-based methods may suffer from the illumination variations and occlusion issue, and local feature correspondence-based methods often do not consider local deformations and the spatial coherence between two point sets. In this paper, we propose a novel and effective Nonrigid Image Matching (NIM) approach to tackle the task of near-duplicate keyframe retrieval from real-world video corpora. In contrast to previous approaches, the NIM technique can recover an explicit mapping between two near-duplicate images with a few deformation parameters and find out the correct correspondences from noisy data effectively. To make our technique applicable to large-scale applications, we suggest an effective multi-level ranking scheme that filters out the irrelevant results in a coarse-to-fine manner. In our ranking scheme, to overcome the extremely small training size challenge, we employ a semi-supervised learning method for improving the performance using unlabeled data. To evaluate the effectiveness of our solution, we have conducted extensive experiments on two benchmark testbeds extracted from the TRECVID2003 and TRECVID2004 corpora. The promising results show that our proposed method is more effective than other state-of-the-art approaches.

Keywords

image copy detection, near-duplicate keyframe, nonrigid image matching, semi-supervised learning

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

MM '08: Proceedings of the 16th ACM International Conference on Multimedia: Vancouver, BC, Canada, October 27-31

First Page

41

Last Page

50

ISBN

9781605583037

Identifier

10.1145/1459359.1459366

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/1459359.1459366

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