Abstract
Multi-view feature extraction is an attractive research topic in computer vision domain, since it can well reveal the inherent property of images. Most existing multi-view feature extraction methods focus on investigating the intra-view or inter-view correlation. However, they fail to consider the sparse reconstruction relationship and the discriminant correlation in multi-view data, simultaneously. In this paper, we propose a novel multi-view feature extraction approach named Multi-view Sparse Embedding Analysis (MSEA). MSEA not only explores the sparse reconstruction relationship that hides in multi-view data, but also considers discriminant correlation by maximizing the within-class correlation and simultaneously minimizing the between-class correlation from intra-view. Moreover, we add orthogonal constraints of embedding matrices to remove the redundancy among views. To tackle the linearly inseparable problem in original feature space, we further provide a kernelized extension of MSEA called KMSEA. The experimental results on two datasets demonstrate the proposed approaches outperform several state-of-the-art related methods.
Y. Zhu and X. Jing—The work described in this paper was fully supported by the National Natural Science Foundation of China under Project No. 61272273, the Research Project of Nanjing University of Posts and Telecommunications under Project No. XJKY14016, and the Postgraduate Scientific Research and Innovation Plan of Jiangsu Province Universities under Project No. CXLX13-465.
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Zhu, Y., Jing, X., Wang, Q., Wu, F., Feng, H., Wu, S. (2015). Multi-view Sparse Embedding Analysis Based Image Feature Extraction and Classification. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds) Computer Vision. CCCV 2015. Communications in Computer and Information Science, vol 547. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48570-5_6
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DOI: https://doi.org/10.1007/978-3-662-48570-5_6
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