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Near-duplicate keyframe retrieval by semi-supervised learning and nonrigid image matching

Near-duplicate keyframe retrieval by semi-supervised learning and nonrigid image matching Near-Duplicate Keyframe Retrieval by Semi-Supervised Learning and Nonrigid Image Matching JIANKE ZHU, Zhejiang University STEVEN C. H. HOI, Nanyang Technological University MICHAEL R. LYU, The Chinese University of Hong Kong SHUICHENG YAN, National University of Singapore Near-duplicate keyframe (NDK) retrieval techniques are critical to many real-world multimedia applications. Over the last few years, we have witnessed a surge of attention on studying near-duplicate image/keyframe retrieval in the multimedia community. To facilitate an effective approach to NDK retrieval on large-scale data, we suggest an effective Multi-Level Ranking (MLR) scheme that effectively retrieves NDKs in a coarse-to- ne manner. One key stage of the MLR ranking scheme is how to learn an effective ranking function with extremely small training examples in a near-duplicate detection task. To attack this challenge, we employ a semi-supervised learning method, semi-supervised support vector machines, which is able to signi cantly improve the retrieval performance by exploiting unlabeled data. Another key stage of the MLR scheme is to perform a ne matching among a subset of keyframe candidates retrieved from the previous coarse ranking stage. In contrast to previous approaches based on either simple heuristics or rigid matching models, we propose a novel Nonrigid Image Matching http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) Association for Computing Machinery

Near-duplicate keyframe retrieval by semi-supervised learning and nonrigid image matching

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2011 by ACM Inc.
ISSN
1551-6857
DOI
10.1145/1870121.1870125
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Abstract

Near-Duplicate Keyframe Retrieval by Semi-Supervised Learning and Nonrigid Image Matching JIANKE ZHU, Zhejiang University STEVEN C. H. HOI, Nanyang Technological University MICHAEL R. LYU, The Chinese University of Hong Kong SHUICHENG YAN, National University of Singapore Near-duplicate keyframe (NDK) retrieval techniques are critical to many real-world multimedia applications. Over the last few years, we have witnessed a surge of attention on studying near-duplicate image/keyframe retrieval in the multimedia community. To facilitate an effective approach to NDK retrieval on large-scale data, we suggest an effective Multi-Level Ranking (MLR) scheme that effectively retrieves NDKs in a coarse-to- ne manner. One key stage of the MLR ranking scheme is how to learn an effective ranking function with extremely small training examples in a near-duplicate detection task. To attack this challenge, we employ a semi-supervised learning method, semi-supervised support vector machines, which is able to signi cantly improve the retrieval performance by exploiting unlabeled data. Another key stage of the MLR scheme is to perform a ne matching among a subset of keyframe candidates retrieved from the previous coarse ranking stage. In contrast to previous approaches based on either simple heuristics or rigid matching models, we propose a novel Nonrigid Image Matching

Journal

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) – Association for Computing Machinery

Published: Jan 1, 2011

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