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Machine Vision, Image Recognition and Machine Learning

[CRTL fro Cross-domain Image Recognition]

Paper:

Shanshan Wang, Lei Zhang*, and Wangmeng Zuo, Class-specific Reconstruction Transfer Learning via Sparse Low-rank Constraint, IEEE Int'Conf. Computer Vision (ICCV), in CEFRL Workshop, Oral presentation, 2017. [paper]

Abstract:

Subspace learning and reconstruction have been widely explored in recent transfer learning work and generally a specially designed projection and reconstruction transfer matrix are wanted. However, existing subspace reconstruction based algorithms neglect the class prior such that the learned transfer function is biased, especially when data scarcity of some class is encountered. Different from those previous methods, in this paper, we propose a novel reconstruction-based transfer learning method called Class-specific Reconstruction Transfer Learning (CRTL), which optimizes a well-designed transfer loss function without class bias. Using a class-specific reconstruction matrix to align the source domain with the target domain which provides help for classification with class prior modeling. Furthermore, to keep the intrinsic relationship between data and labels after feature augmentation, a projected Hilbert-Schmidt Independence Criterion (pHSIC), that measures the dependency between two sets, is first proposed by mapping the data from original space to RKHS in transfer learning. In addition, combining low-rank and sparse constraints on the class-specific reconstruction coefficient matrix, the global and local data structures can be effectively preserved. Extensive experiments demonstrate that the proposed method outperforms conventional representationbased domain adaptation methods.

Approach:

Illustration of our proposed Class-specific Reconstruction Transfer Learning (CRTL)

Experiments:

Recognition accuracy (%) of different domain adaptation over 10 object categories on 4DA-CNN with deep feature representation, COIL-20, face recognition across poses, and handwritten digits recognition.

References:

[1] L. Zhang,W. Zuo, and D. Zhang. Lsdt: Latent sparse domain transfer learning for visual adaptation. IEEE Trans Image Process, 25(3):1177–1191, 2016.

[2] Y. Xu, X. Fang, J. Wu, X. Li, and D. Zhang. Discriminative transfer subspace learning via low-rank and sparse representation. IEEE Trans Image Process, 25(2):850–863, 2015.

[3] M. Shao, D. Kit, and Y. Fu. Generalized transfer subspace learning through low-rank constraint. IJCV, 109(1):74–93, 2014.

[4] G. Liu, Z. Lin, and Y. Yu. Robust subspace segmentation by low-rank representation. In ICML, pages 663–670, 2010.