

FOLLOWUS
1.School of Information Engineering, Jiaxing University, Jiaxing 314001, China
2.School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
[ "Rong ZHU, E-mail: sikexing@163.com" ]
[ "Min YAO, E-mail: myao@zju.edu.cn" ]
Received:12 February 2012,
Revised:2012-;9-11,
Published:2012-10
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Rong ZHU, Min YAO, Li-hua YE, et al. Learning a hierarchical image manifold for Web image classification[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(10): 719-735.
Rong ZHU, Min YAO, Li-hua YE, et al. Learning a hierarchical image manifold for Web image classification[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(10): 719-735. DOI: 10.1631/jzus.C1200032.
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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