.png){kind=icon}
返回
Effective texture classification by texton encoding induced statistical features
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2014.08.014.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Effective and efficient texture feature extraction and classification is an important problem in image understanding and recognition. Recently, texton learning based texture classification approaches have been widely studied, where the textons are usually learned via K-means clustering or sparse coding methods. However, the K-means clustering is too coarse to characterize the complex feature space of textures, while sparse texton learning/encoding is time-consuming due to the l(0)-norm or l(1)-norm minimization. Moreover, these methods mostly compute the texton histogram as the statistical features for classification, which may not be effective enough. This paper presents an effective and efficient texton learning and encoding scheme for texture classification. First, a regularized least square based texton learning method is developed to learn the dictionary of textons class by class. Second, a fast two-step l(2)-norm texton encoding method is proposed to code the input texture feature over the concatenated dictionary of all classes. Third, two types of histogram features are defined and computed from the texton encoding outputs: coding coefficients and coding residuals. Finally, the two histogram features are combined for classification via a nearest subspace classifier. Experimental results on the CUReT, KTH_TIPS and UIUC datasets demonstrated that the proposed method is very promising, especially when the number of available training samples is limited. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
Texture classification
Texton learning
Texture feature extraction
Sparse representation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Representing and recognizing the visual appearance of materials using three-dimensional textons
.png){kind=icon}
PREAI
分享收藏
Graphene/Ionic Liquid Binary Electrode Material for High Performance Supercapacitor用于高性能超级电容器的石墨烯/离子液体二元电极材料
PREAI
分享收藏
查看更多内容