Projected Transfer Sparse Coding for cross domain image representation

• We project source and target data in a low dimensional space.
• We consider the distribution divergence and correlation of source and target data.
• Source samples are reweighted according to target domain.
• A unified objective is presented to learn the projection matrix and sparse coding.
• Our algorithm can be transformed to KSVD method which is simple and effective.

Sparse coding has been used for image representation successfully. However, when there is considerable variation between source and target domain, sparse coding cannot achieve satisfactory results. In this paper, we proposed a Projected Transfer Sparse Coding algorithm. In order to reduce their distribution difference, we project source and target data into a shared low dimensional space. Meanwhile, we learn a projection matrix and a shared dictionary and the sparse coding of source and target data in the low dimensional space. Unlike existing methods, the sparse representations are learnt using the projected data which are invariant to the distribution difference and the irrelevant samples. Thus, the sparse representations are robust and can improve the classification performance. We do not need to know any explicit correspondence across domains. We learn the projection matrix, the discriminative sparse representations, and the dictionary in a unified objective function. Our image representation method yields state-of-the-art results.

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