کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4947363 | 1439575 | 2017 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Unsupervised segmentation of natural images based on statistical modeling
ترجمه فارسی عنوان
تقسیم بندی ناپیوسته تصاویر طبیعی بر اساس مدل آماری
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
A novel unsupervised scheme for natural image segmentation is proposed aiming to acquire perceptually consistent results. Firstly, comprehensive visual features besides raw color values are extracted, including spatial frequency, contrast sensitivity, color deviation, and so on. Secondly, high correlations among visual features are reduced via principal component analysis (PCA) and the raw image pixels are then converted to a collection of feature vectors in a multi-dimensional feature space. Thirdly, the Gaussian mixture model (GMM) is employed to approximate the class distribution of image pixels and an improved expectation maximization (EM) algorithm is introduced to estimate model parameters. Finally, segmentation results are obtained by grouping of pixels based on the mixture components. Experiments are conducted and the results demonstrate that, compared with existing techniques, the proposed scheme can acquire more perceptually consistent results.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 252, 23 August 2017, Pages 95-101
Journal: Neurocomputing - Volume 252, 23 August 2017, Pages 95-101
نویسندگان
Zhu Zhong-jie, Wang Yu-er, Jiang Gang-yi,