Abstract
This paper proposes a novel framework for image classification with an entropy based image semantic cycle. Entropy minimization leads to an optimal image semantic cycle where images are connected in the semantic order. For classification, the training step is to find an optimal image semantic cycle in an image database. In the test step, the suitable position of an unknown image in this cycle is first found. Then, the class membership is determined through recognizing the nearest neighbors at this position. Experimental results demonstrate that the proposed framework achieves higher classification accuracy.
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© 2012 Springer-Verlag Berlin Heidelberg
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Li, H., Niu, J., Zhang, L. (2012). Entropy Based Image Semantic Cycle for Image Classification. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34500-5_63
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DOI: https://doi.org/10.1007/978-3-642-34500-5_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34499-2
Online ISBN: 978-3-642-34500-5
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