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Front. Inform. Technol. Electron. Eng.  2012, Vol. 13 Issue (10): 719-735    DOI: 10.1631/jzus.C1200032
    
Learning a hierarchical image manifold for Web image classification
Rong Zhu, Min Yao, Li-hua Ye, Jun-ying Xuan
School of Information Engineering, Jiaxing University, Jiaxing 314001, China; School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Learning a hierarchical image manifold for Web image classification
Rong Zhu, Min Yao, Li-hua Ye, Jun-ying Xuan
School of Information Engineering, Jiaxing University, Jiaxing 314001, China; School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
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摘要： 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.
关键词： Web image classification;  Manifold learning;  Image manifold;  Semantic granularity;  Distance measure    
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.
Key words: Web image classification    Manifold learning    Image manifold    Semantic granularity    Distance measure
收稿日期: 2012-02-12 出版日期: 2012-10-01
CLC:  TP391  
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Rong Zhu
Min Yao
Li-hua Ye
Jun-ying Xuan

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Rong Zhu, Min Yao, Li-hua Ye, Jun-ying Xuan. Learning a hierarchical image manifold for Web image classification. Front. Inform. Technol. Electron. Eng., 2012, 13(10): 719-735.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1200032    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2012/V13/I10/719

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