کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
406297 678076 2015 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Local Gabor maximum edge position octal patterns for image retrieval
ترجمه فارسی عنوان
محلی گابور حداکثر موقعیت لبه الگوهای هشت ضلعی برای بازیابی تصویر
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

In this paper, a new coding scheme, local Gabor maximum edge position octal patterns (LGMEPOP) is proposed for content based image retrieval. The standard local binary pattern (LBP) collects the sign edge (binary code) information between the center pixel and its surrounding neighbors in an image. Further, the concept of LBP is extended to local maximum edge binary pattern (LMEBP) which collects the sign code (binary code) using the magnitude edges. These magnitude edges are collected based on the maximum edges between the center pixel and its surrounding neighbors in an image. In this paper, we propose a new feature descriptor, octal code which is coded based on the maximum edge positions (MEP) on Gabor responses. Specially, each pixel of every Gabor response gains eight edges based on the relationship between the referenced pixel and its neighbors. LGMEPOP utilizes the first three dominant (maximum) edge positions in an octal code generation. Then, these three maximum edge positions are encoded into three-eight octal numbers to produce the LGMEPOP. Further, the LGMEPOP is classified into two categories which are named as sign maximum edge position octal pattern (SMEPOP) and magnitude maximum edge position octal pattern (MMEPOP). The SMEPOP and MMEPOP are coded based on the sign and magnitudes of dominant edges respectively. The performance of the proposed method is tested on two benchmark databases. The results after being investigated show a significant improvement as compared to other existing methods (LBP and LBP variants) in terms of average retrieval precision (ARP) and average retrieval rate (ARR) on Corel-5000 and Corel-10000 databases.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 167, 1 November 2015, Pages 336–345
نویسندگان
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