Abstract
Local Binary Pattern (LBP) has been widely used in texture analysis and content-based image retrieval (CBIR). LBP encodes the relationship between the referenced pixel and its surrounding neighbors by computing gray-level variation. However, LBP is unable to reflect the spatial distribution information of gray variation direction in the whole image. Therefore, in this paper, we propose a new texture direction feature descriptor to extract the spatial distribution information of gray-level variation between pixels. After the calculation of the gray variation pattern on different directions, we construct the statistic histograms of pattern pairs between the referenced pixel and its neighbor pixels. The performance of the proposed feature descriptor is compared with different methods using two benchmark image databases. Performance analysis shows that the proposed feature descriptor improves the retrieval precision rate, as well as the recall rate both in texture and natural scene images.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant No. 61272317) and the General Program of Natural Science Foundation of AnHui of China (Grant No. 1208085MF90).
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Xia, Y., Wan, S., Yue, L. (2014). A New Texture Direction Feature Descriptor and Its Application in Content-Based Image Retrieval. In: Farag, A., Yang, J., Jiao, F. (eds) Proceedings of the 3rd International Conference on Multimedia Technology (ICMT 2013). Lecture Notes in Electrical Engineering, vol 278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41407-7_14
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DOI: https://doi.org/10.1007/978-3-642-41407-7_14
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