Abstract
After segmenting the image into several sub-images, each sub-image is taken through three level wavelet transform, and then the texture images are obtained. Meanwhile, the distributions of each sub-image’s information entropy are calculated. Such a way, both the global wavelet texture information and the spatial distribution of information entropy are effectively used as the main retrieval characteristics. On this basis, the genetic clustering algorithm used for the image clustering, and the likelihood between the query example image and corresponding image’s cluster center is calculated. Experimental results show that the method presented in this paper has good retrieval performance.
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© 2012 Springer-Verlag Berlin Heidelberg
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Moydin, K., Hamdulla, A. (2012). Wavelet Domain Distributed Information Entropy and Genetic Clustering Algorithm for Image Retrieval. In: Wang, F.L., Lei, J., Lau, R.W.H., Zhang, J. (eds) Multimedia and Signal Processing. CMSP 2012. Communications in Computer and Information Science, vol 346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35286-7_12
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DOI: https://doi.org/10.1007/978-3-642-35286-7_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35285-0
Online ISBN: 978-3-642-35286-7
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