• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Domain Adaptation Image Classification Based on Multi-sparse Representation

Vol. 11, No.5, May 31, 2017
10.3837/tiis.2017.05.016, Download Paper (Free):

Abstract

Generally, research of classical image classification algorithms assume that training data and testing data are derived from the same domain with the same distribution. Unfortunately, in practical applications, this assumption is rarely met. Aiming at the problem, a domain adaption image classification approach based on multi-sparse representation is proposed in this paper. The existences of intermediate domains are hypothesized between the source and target domains. And each intermediate subspace is modeled through online dictionary learning with target data updating. On the one hand, the reconstruction error of the target data is guaranteed, on the other, the transition from the source domain to the target domain is as smooth as possible. An augmented feature representation produced by invariant sparse codes across the source, intermediate and target domain dictionaries is employed for across domain recognition. Experimental results verify the effectiveness of the proposed algorithm.


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Cite this article

[IEEE Style]
Xu Zhang, Xiaofeng Wang, Yue Du and Xiaoyan Qin, "Domain Adaptation Image Classification Based on Multi-sparse Representation," KSII Transactions on Internet and Information Systems, vol. 11, no. 5, pp. 2590-2606, 2017. DOI: 10.3837/tiis.2017.05.016

[ACM Style]
Zhang, X., Wang, X., Du, Y., and Qin, X. 2017. Domain Adaptation Image Classification Based on Multi-sparse Representation. KSII Transactions on Internet and Information Systems, 11, 5, (2017), 2590-2606. DOI: 10.3837/tiis.2017.05.016