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On-line Access: 2012-10-01

Received: 2012-02-12

Revision Accepted: 2012-07-31

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.10 P.719-735

10.1631/jzus.C1200032


Learning a hierarchical image manifold for Web image classification


Author(s):  Rong Zhu, Min Yao, Li-hua Ye, Jun-ying Xuan

Affiliation(s):  School of Information Engineering, Jiaxing University, Jiaxing 314001, China; more

Corresponding email(s):   sikexing@163.com, myao@zju.edu.cn

Key Words:  Web image classification, Manifold learning, Image manifold, Semantic granularity, Distance measure


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Rong Zhu, Min Yao, Li-hua Ye, Jun-ying Xuan. Learning a hierarchical image manifold for Web image classification[J]. Journal of Zhejiang University Science C, 2012, 13(10): 719-735.

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%P 719-735
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%DOI 10.1631/jzus.C1200032

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T1 - Learning a hierarchical image manifold for Web image classification
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A1 - Min Yao
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1200032


Abstract: 
Image classification is an essential task in content-based image retrieval. However, due to the semantic gap between low-level visual features and high-level semantic concepts, and the diversification of Web images, the performance of traditional classification approaches is far from users’ expectations. In an attempt to reduce the semantic gap and satisfy the urgent requirements for dimensionality reduction, high-quality retrieval results, and batch-based processing, we propose a hierarchical image manifold with novel distance measures for calculation. Assuming that the images in an image set describe the same or similar object but have various scenes, we formulate two kinds of manifolds, object manifold and scene manifold, at different levels of semantic granularity. Object manifold is developed for object-level classification using an algorithm named extended locally linear embedding (ELLE) based on intra- and inter-object difference measures. Scene manifold is built for scene-level classification using an algorithm named locally linear submanifold extraction (LLSE) by combining linear perturbation and region growing. Experimental results show that our method is effective in improving the performance of classifying Web images.

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