کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10321807 660751 2015 44 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Improving the descriptors extracted from the co-occurrence matrix using preprocessing approaches
ترجمه فارسی عنوان
بهبود توصیفگرهای استخراج شده از ماتریس همکاری با استفاده از روش های پیش پردازش
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
In this paper, we investigate the effects that different preprocessing techniques have on the performance of features extracted from Haralick's co-occurrence matrix, one of the best known methods for analyzing image texture. In addition, we compare and combine different strategies for extracting descriptors from the co-occurrence matrix. We propose an ensemble of different preprocessing methods, where, for each descriptor, a given Support Vector Machine (SVM) classifier is trained. The set of classifiers is then combined by weighted sum rule. The best result is obtained by combining the extracted descriptors using the following preprocessing methods: wavelet decomposition, local phase quantization, orientation, and the Weber law descriptor. Texture descriptors are extracted from the entire co-occurrence matrix, as well as from sub-windows, and evaluated at multiple scales. We validate our approach on eleven image datasets representing different image classification problems using the Wilcoxon signed rank test. Results show that our approach improves the performance of standard methods. All source code for the approaches tested in this paper will be available at: https://www.dei.unipd.it/node/2357
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 42, Issue 22, 1 December 2015, Pages 8989-9000
نویسندگان
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