Abstract
In content-based image retrieval (CBIR), most techniques involve an important issue of how to efficiently bridge the gap between the high-level concepts and low-level visual features. We propose a novel semi-supervised learning method for image retrieval, which makes full use of both ICA feature and general low-level feature. Our approach can be characterized by the following three aspects: (1) The ICA feature, as proved to be representative of human vision, is adopted as a view to describe human perception; (2) A multi-view learning algorithm is introduced to make the most use of different features and dramatically reduce human relevance feedback needed to achieve a satisfactory result; (3) A new semi-supervised learning algorithm is proposed to adapt to the small sample problem and other special constrains of our multi-view learning algorithm. Our experimental results and comparisons are presented to demonstrate the effectiveness of the proposed approach.
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Wang, F., Dai, Q. (2006). A New Multi-view Learning Algorithm Based on ICA Feature for Image Retrieval. In: Cham, TJ., Cai, J., Dorai, C., Rajan, D., Chua, TS., Chia, LT. (eds) Advances in Multimedia Modeling. MMM 2007. Lecture Notes in Computer Science, vol 4351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69423-6_44
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DOI: https://doi.org/10.1007/978-3-540-69423-6_44
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