کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4969748 | 1449985 | 2017 | 38 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Local binary features for texture classification: Taxonomy and experimental study
ترجمه فارسی عنوان
ویژگی های باینری محلی برای طبقه بندی بافت: طبقه بندی و مطالعه تجربی
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
طبقه بندی بافت، الگوی دودویی محلی، انحراف چرخش، استحکام نویز، یادگیری عمیق،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
In this paper, we provide a systematic review of current LBP variants and propose a taxonomy to more clearly group the prominent alternatives. Merits and demerits of the various LBP features and their underlying connections are also analyzed. We perform a large scale performance evaluation for texture classification, empirically assessing forty texture features including thirty two recent most promising LBP variants and eight non-LBP descriptors based on deep convolutional networks on thirteen widely-used texture datasets. The experiments are designed to measure their robustness against different classification challenges, including changes in rotation, scale, illumination, viewpoint, number of classes, different types of image degradation, and computational complexity. The best overall performance is obtained for the Median Robust Extended Local Binary Pattern (MRELBP) feature. For textures with very large appearance variations, Fisher vector pooling of deep Convolutional Neural Networks is clearly the best, but at the cost of very high computational complexity. The sensitivity to image degradations and computational complexity are among the key problems for most of the methods considered.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 62, February 2017, Pages 135-160
Journal: Pattern Recognition - Volume 62, February 2017, Pages 135-160
نویسندگان
Li Liu, Paul Fieguth, Yulan Guo, Xiaogang Wang, Matti Pietikäinen,