Journal
SIGNAL PROCESSING
Volume 93, Issue 6, Pages 1597-1607Publisher
ELSEVIER
DOI: 10.1016/j.sigpro.2012.05.012
Keywords
Object classification; Graph kernel; Multi-view segment graph
Categories
Funding
- National Natural Science Foundation of China [61170142]
- National Key Technology RD Program [2011BAG05B04]
- Program for New Century Excellent Talents in University [NCET-09-0685]
- Fundamental Research Funds for the Central Universities [2010QNA5006]
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Object classification is an important issue in multimedia information retrieval. Usually, we can use images from multiple views (or multi-view images) to describe an object for classification. However, two issues remain unsolved. First, exploiting the spatial relations of local features from different view images for object classification. Second, accelerating the multi-view object classification process. To solve these two problems, we propose fast multi-view segment graph kernel (FMSGK). Given a set of multi-view images for an object, we segment each of them in terms of its color intensity distribution. And inter- and intra-view segment graphs are constructed to describe the spatial relations of the segments between and within view images respectively. Then, these two types of graphs are integrated into a so-called multi-view segment graph. And the kernel between objects is computed by accumulating all matchings' of walk structures between their corresponding multi-view segment graphs. Since computing the kernel directly is highly time-consuming, an accelerating algorithm is derived. Finally, a multi-class support vector machine (SVM) (Duda et al., 2000 [19]; Wang et al., 2008 [32]; Dai and Mai, 2012 [6]) is trained based on the computed kernels for object classification. The experimental results on three data sets validate the effectiveness of our approach. (C) 2012 Elsevier B.V. All rights reserved.
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