الگوی سه بعدی محلی جهت: یک توصیفگر بافت جدید برای طبقه بندی بافت
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|157227||2018||14 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Computer Vision and Image Understanding, Volume 169, April 2018, Pages 14-27
In this paper, the three level descriptions from LTP and the directional features from LDP are combined to form a new local feature descriptor, referred to as local directional ternary pattern (LDTP) for texture classification. LDTP is a framework, which consists in encoding both contrast information and directional pattern features in a compact way based on local derivative variations. To achieve robustness, the proposed operator first computes for each pixel within its 3â¯Ãâ¯3 overlapping grayscale image patch, on the one hand, eight directional edge responses using the eight FreiâChen masks, and on the other hand, central edge response through the 2nd derivative of Gaussian filter to capture more detailed information. This allows producing a more discriminative encoding than several state-of-the art methods based only on intensity information. Then, spatial relationships among the neighboring pixels through the edge responses are exploited independently with the help of both LDPâs and LTPâs concepts to enhance the discrimination capability. Indeed, the implicit utilization of both concepts of LTP and LDP encodes more information in comparison to the existing directional and derivative methods in less space, and simultaneously allows discriminating more textures. Finally, the resultant LDTP pattern is divided into two distinct parts: local directional ternary pattern upper (LDTPU) and local directional ternary pattern lower (LDTPL), and the final feature descriptor vector is obtained by linear concatenation of both LDTPU and LDTPL histograms. The experiments carried out on nine publicly available texture datasets demonstrated that the proposed LDTP descriptor achieves classification performance, which is competitive or better than several recent and old state-of-the-art LBP variants. Statistical significance of the achieved accuracy improvement by the proposed descriptor has been also demonstrated through the Wilcoxon signed rank test applied on all the tested datasets.