《计算机应用研究》|Application Research of Computers

基于随机特征字典的纹理分类方法

Texture classification method via random feature dictionary

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作者 沈仁明,徐小红,王教余,廖重阳
机构 合肥工业大学 计算机与信息学院,合肥 230009
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文章编号 1001-3695(2015)01-0303-04
DOI 10.3969/j.issn.1001-3695.2015.01.071
摘要 为解决稀疏表示在提取全局纹理特征时受维数限制的问题,提出一种基于随机特征字典的特征提取及分类方法。方法利用稀疏系数中非零系数的分布特点,统计各图像块在稀疏分解过程中字典原子的使用频率,得到能突出纹理在稀疏域类别信息的直方图特征,进而实现分类。为提高分类准确率,通过随机投影将多尺度多方向的小波特征进行融合,并对其训练得到纹理描述能力更强的小波随机特征字典。在分类实验中,其分类准确率达94.79%,并能在噪声、光照条件影响下获得较好的鲁棒性,在分析全局纹理特征方面具有高效、稳定的特点。
关键词 稀疏表示;字典学习;纹理分类;纹理全局特征提取
基金项目 安徽省自然科学基金项目(128085MF91)
国家重大科研装备研制项目(ZDYZ2012-1)
本文URL http://www.arocmag.com/article/01-2015-01-071.html
英文标题 Texture classification method via random feature dictionary
作者英文名 SHEN Ren-ming, XU Xiao-hong, WANG Jiao-yu, LIAO Chong-yang
机构英文名 School of Computer & Information, Hefei University of Technology, Hefei 230009, China
英文摘要 Extracting global texture feature through sparse representation faced some problems, which mainly caused by high dimension. In order to solve those problems, this paper proposed a feature extraction and classification method based on random feature dictionary. The proposed method utilized the distribution of non-zero coefficients, which were computed by sparse decomposition, to generate a statistics histogram feature. The acquired histogram could reflect the dictionary atoms’ using frequency in sparse decomposition, and was able to reflect the class information. Thus, the classification could be realized. For the sake of improving classification accuracy, it fused multi-scale and multi-direction wavelet features through random projection, and then trained a more descriptive dictionary by those fused features. In the classification experiments, it achieved 94.79% classification accuracy. Further experiments and analysis prove that the proposed method is robust under noisy and bad illumination conditions, and has characteristics of effective and stable in global texture feature extraction.
英文关键词 sparse representation; dictionary learning; texture classification; global texture feature extraction
参考文献 查看稿件参考文献
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收稿日期 2013/12/4
修回日期 2014/1/27
页码 303-306
中图分类号 TP391.41
文献标志码 A