کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6939097 | 1449968 | 2018 | 42 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Median local ternary patterns optimized with rotation-invariant uniform-three mapping for noisy texture classification
ترجمه فارسی عنوان
الگوهای محلی سه گانه محلی بهینه شده با یکنواخت چرخش بدون تغییر یکنواخت-سه نقشه برداری برای طبقه بندی بافت پر سر و صدا
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کلمات کلیدی
طبقه بندی بافت پر سر و صدا الگوی محلی سه گانه محلی، یکنواخت چرخش-یکنواخت-سه نقشه برداری، توزیع مشترک چند منظوره،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Local ternary pattern (LTP) is a simple, yet high-discriminative texture feature model. In order to further promote the performance of LTP in noisy texture classification, this paper proposes a multi-scale median LTP (MLTP) framework. It firstly designs a non-overlapped median sampling scheme to resist against noise, and then re-defines central descriptor, radial descriptor and magnitude descriptor for MLTP. Moreover, it also proposes the pattern mapping solution named rotation-invariant uniform three (riu3) so as to project original MLTP codes from high-dimensional patterns to low-dimensional ones. In our multi-scale algorithm, every image is sampled and encoded separately by three MLTP descriptors, and then original patterns are mapped by a pre-stored riu3 pattern lookup table. Finally, the frequency histogram of joint distribution on three mapped MLTP code images is computed to generate the feature vector of given sampling scale. The different vectors under different scales are concatenated together to produce the objective vectors for training classifier or texture classification. The available classifiers for our algorithm include nearest neighbor classifier (NNC), and support vector machine (SVM), etc. The experiments on five publicly-available texture databases show that the multi-scale MLTP method proposed in this paper could obtain an average accuracy of 99.48% on dataset OTC12, 96.97% on UIUC, 98.71% on KTH-TIPS2b, 98.55% on Brodatz and 97.49% on ALOT. Compared with other state-of-the-art approaches, under the low-intensity conditions of Gaussian, Salt & Pepper and random pixel corruption noise, multi-scale MLTPÂ +Â SVM could still keep higher accuracy and have better noise robustness than others.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 79, July 2018, Pages 387-401
Journal: Pattern Recognition - Volume 79, July 2018, Pages 387-401
نویسندگان
Luping Ji, Yan Ren, Xiaorong Pu, Guisong Liu,