کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
526784 869228 2015 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Globally rotation invariant multi-scale co-occurrence local binary pattern
ترجمه فارسی عنوان
الگوریتم دودویی محلی که در مقیاس جهانی مقیاس پذیری چرخش جهانی است
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی


• This paper proposes a globally rotation invariant multi-scale co-occurrence of LBPs (MCLBP).
• The proposed MCLBP can effectively capture the correlation between the LBPs in different scales.
• Three globally rotation invariant encoding methods are introduced for MCLBP.
• The proposed MCLBP performs very well on texture, material, and medical cell classification.

This paper proposes a globally rotation invariant multi-scale co-occurrence local binary pattern (MCLBP) feature for texture-relevant tasks. In MCLBP, we arrange all co-occurrence patterns into groups according to properties of the co-patterns, and design three encoding functions (Sum, Moment, and Fourier Pooling) to extract features from each group. The MCLBP can effectively capture the correlation information between different scales and is also globally rotation invariant (GRI). The MCLBP is substantially different from most existing LBP variants including the LBP, the CLBP, and the MSJ-LBP that achieves rotation invariance by locally rotation invariant (LRI) encoding. We fully evaluate the properties of the MCLBP and compare it with some powerful features on five challenging databases. Extensive experiments demonstrate the effectiveness of the MCLBP compared to the state-of-the-art LBP variants including the CLBP and the LBPHF. Meanwhile, the dimension and computational cost of the MCLBP is also lower than that of the CLBP_S/M/C and LBPHF_S_M.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Image and Vision Computing - Volume 43, November 2015, Pages 16–26
نویسندگان
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