Özet:

Texture recognition is one of the active fields in pattern recognition. Researchers have been searching for the best representation of a texture image for decades. The majority of methods use appearance-based properties of texture images to generate a feature descriptor. In this paper, we propose novel feature descriptor, namely histogram of edge segment curvatures (HESC) which extracts edge segments of an input image and construct a histogram from quantized curvature values of them. Therefore, HESC unveils geometric information of texture images by utilizing curve strengths for each pixel along the edge segments. We show that the proposed feature descriptor is robust against rotation and translation. We also extend HESC descriptor to emphasis the contribution of small curvature values. We carry out several experiments in UIUC texture dataset and compare the performance of the proposed HESC descriptor to well-known Local Binary Pattern (LBP). The proposed texture descriptor outperforms LBP in terms of recognition accuracy.

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