基于随机特征字典的纹理分类方法
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。...
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| Published in | 计算机应用研究 Vol. 32; no. 1; pp. 303 - 306 |
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| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
合肥工业大学计算机与信息学院,合肥,230009
2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-3695 |
| DOI | 10.3969/j.issn.1001-3695.2015.01.071 |
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| Abstract | 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。 |
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| AbstractList | TP391.41; 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. 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。 |
| Author | 沈仁明 徐小红 王教余 廖重阳 |
| AuthorAffiliation | 合肥工业大学计算机与信息学院,合肥230009 |
| AuthorAffiliation_xml | – name: 合肥工业大学计算机与信息学院,合肥,230009 |
| Author_FL | XU Xiao-hong WANG Jiao-yu LIAO Chong-yang SHEN Ren-ming |
| Author_FL_xml | – sequence: 1 fullname: SHEN Ren-ming – sequence: 2 fullname: XU Xiao-hong – sequence: 3 fullname: WANG Jiao-yu – sequence: 4 fullname: LIAO Chong-yang |
| Author_xml | – sequence: 1 fullname: 沈仁明 徐小红 王教余 廖重阳 |
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| DocumentTitleAlternate | Texture classification method via random feature dictionary |
| DocumentTitle_FL | Texture classification method via random feature dictionary |
| EndPage | 306 |
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| Keywords | 纹理全局特征提取 字典学习 纹理分类 global texture feature extraction dictionary learning texture classification 稀疏表示 sparse representation |
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| Notes | 51-1196/TP sparse representation; dictionary learning; texture classification; global texture feature extraction 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 ran- dom 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 fre- quency 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 projec- tion, and then trained a more descriptive dictionary by those fused features. In the classification experiments, it achieved 94.79% classification accuracy. Further |
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| PublicationTitle | 计算机应用研究 |
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| PublicationYear | 2015 |
| Publisher | 合肥工业大学计算机与信息学院,合肥,230009 |
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| StartPage | 303 |
| SubjectTerms | 字典学习 稀疏表示 纹理全局特征提取 纹理分类 |
| Title | 基于随机特征字典的纹理分类方法 |
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