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Neural Computing and Applications

Erschienen in:

01.06.2013 | ICONIP 2011

Combining affinity propagation with supervised dictionary learning for image classification

verfasst von: Bingxin Xu, Rukun Hu, Ping Guo

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2013

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Abstract

Recently support vector machines (SVM) using spatial pyramid matching (SPM) kernel have been highly successful in image classification applications. And linear spatial pyramid matching using sparse coding (ScSPM) scheme has been proposed to enhance the performance of SPM both in time and classification accuracy. In order to reduce the time complexity of dictionary construction process, sparse coding with affinity propagation method has been proposed in this paper. Because the dictionary used for sparse coding plays a key role in these methods, we also adopt supervised dictionary learning method to construct dictionary. The coding coefficients of each class have greater separability for SVM classification. Substantial experiments on Scene15 and CalTech101 image datasets have been conducted to investigate the performance of proposed approach in multi-class image classification; the results show that the approach can reach higher accuracy compared with ScSPM.

Vorheriger Artikel HEALPIX DCT technique for compressing PCA-based illumination adjustable images

Nächster Artikel Proximity multi-sphere support vector clustering

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Titel: Combining affinity propagation with supervised dictionary learning for image classification
verfasst von: Bingxin Xu
Rukun Hu
Ping Guo
Publikationsdatum: 01.06.2013
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 7-8/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-0957-7

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