Abstract
In this paper, a new method for image texture representation is proposed, which represents image content using a 49 dimensional feature vector through calculating the variation of texture direction and the intensity of texture. In addition, the texture feature is grouped into a feature set with some other image texture representation methods, and then a new online feature selection method with a novel discrimination criterion is presented. We test the discriminating ability of every feature in the feature set utilizing the discrimination criterion, and select the optimal feature subset, which expresses image content in an even better fashion. The results of the computer simulation experiments show that the proposed feature extraction and feature selection method can represent image content effectively, and improve the retrieval precision visibly.
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© 2015 Springer International Publishing Switzerland
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Ma, X., Yu, X. (2015). Image Retrieval Based on Texture Direction Feature and Online Feature Selection. In: Hu, X., Xia, Y., Zhang, Y., Zhao, D. (eds) Advances in Neural Networks – ISNN 2015. ISNN 2015. Lecture Notes in Computer Science(), vol 9377. Springer, Cham. https://doi.org/10.1007/978-3-319-25393-0_24
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DOI: https://doi.org/10.1007/978-3-319-25393-0_24
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