Transfer Kernel Sparse Coding Based on Dynamic Distribution Alignment for Image Representation

Sparse coding based domain adaptation methods aim to learn a robust transfer classifier by utilizing the knowledge from source domain and the learned new representation of both domains. Most existing works have achieved remarkable results in solving linear domain shift problems, but have poor perfor...

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Veröffentlicht in:2024 4th International Conference on Consumer Electronics and Computer Engineering (ICCECE) S. 230 - 234
Hauptverfasser: Huang, Wei, Gan, Min, Chen, Guangyong
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 12.01.2024
Schlagworte:
distribution alignment
domain adaptation
Encoding
Feature extraction
Image coding
Image representation
Sparse approximation
sparse coding
Transfer learning
Visualization
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Zusammenfassung:Sparse coding based domain adaptation methods aim to learn a robust transfer classifier by utilizing the knowledge from source domain and the learned new representation of both domains. Most existing works have achieved remarkable results in solving linear domain shift problems, but have poor performance in nonlinear domain shift problems. In this paper, we propose a Transfer kernel sparse coding based on dynamic distribution alignment (TKSC-DDA) approach for cross-domain visual recognition, which incorporates dynamic distributed alignment into kernel sparse coding to learn discriminative and robust sparse representations. Extensive experiment on visual transfer learning tasks demonstrate that our proposed method can significantly out-perform serval state-of-the-art approaches.
DOI:10.1109/ICCECE61317.2024.10504150