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• W2057440492 abstract "Recently sparse coding with spatial pyramid matching method has shown its excellent performance in image classification. Inspired by this technique, we present an image classification approach by learning the optimal Multiple Pooling Combination strategy based on Non-Negative Sparse Coding (MPC-NNSC) in this paper. First, non-negative sparse coding with three different pooling methods as well as spatial pyramid matching method are utilized to encode local descriptors for image representation, respectively. Then a promising weight learning approach is employed to find a set of optimal weights for best fusing all these pooling methods in different scales. Lastly, support vector machine classifier with linear and histogram intersection kernel is employed for the final classification task. Experiments on two popular benchmark datasets are presented and they demonstrate the better performance of the proposed scheme compared to the state-of-the-art methods." @default.
• W2057440492 created "2016-06-24" @default.
• W2057440492 creator A5043477487 @default.
• W2057440492 creator A5068851817 @default.
• W2057440492 date "2012-06-01" @default.
• W2057440492 modified "2023-09-22" @default.
• W2057440492 title "Learning multiple pooling combination for image classification" @default.
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• W2057440492 doi "https://doi.org/10.1109/ijcnn.2012.6252840" @default.
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