Abstract
It has been shown that support vector machines (SVM) can be used in content-based image retrieval. Existing SVM based methods only extract low-level global or region-based features to form feature vectors and use traditional non-structured kernel function. However, these methods rarely consider the image structure or some new structured kernel types. In order to bridge the semantic gap between low-level features and high-level concepts, in this paper, a novel graph kernel based SVM method is proposed, which takes into account both low-level features and structural information of the image. Firstly, according to human selective visual attention model, for a given image, salient regions are extracted and the concept of Salient Region Adjacency Graph (SRAG) is proposed to represent the image semantics. Secondly, based on the SRAG, a novel graph kernel based SVM is constructed for image semantic retrieval. Experiments show that the proposed method shows better performance in image semantic retrieval than traditional method.
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Feng, S., Xu, D., Yang, X., Geng, Y. (2006). A Novel Graph Kernel Based SVM Algorithm for Image Semantic Retrieval. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_87
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DOI: https://doi.org/10.1007/11760023_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
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