Abstract
Object classification is an important problem in multimedia information retrieval. In order to better objects classification, we often employ a set of multi-view images to describe an object for classification. However, two issues remain unsolved: 1) exploiting the spatial relations of local features in the multi-view images for classification, and 2) accelerating the classification process. To solve them, Fast Multi-view Graph Kernel (FMGK), is proposed. Given a set of multi-view images for an object, we segment each view image into several regions. And inter- and intra- view linkage graphs are constructed to describe the spatial relations of the regions between and within each multi-view image respectively. Then, the inter- and intra- view graphs are integrated into a so-called multi-view region graph. Finally, the kernel between objects is computed by accumulating all matchings’ of walk structures between corresponding multi-view region graphs. And a SVM [11] classifier is trained based on the computed kernels for object classification. The experimental results on different datasets validate the effectiveness of our FMGK.
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Zhang, L., Song, M., Bu, J., Chen, C. (2011). Fast Multi-view Graph Kernels for Object Classification. In: Wang, D., Reynolds, M. (eds) AI 2011: Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science(), vol 7106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25832-9_58
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DOI: https://doi.org/10.1007/978-3-642-25832-9_58
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