基于层次匹配下多种特征融合的蕾丝花边检索方法
针对基于图像纹理特征的蕾丝花边检索方法效率低下问题，为提高蕾丝花边检索效率，提出一种基于层次匹配下多种特征融合的蕾丝花边检索方法。通过运用图像纹理特征标识图像，利用Canny算子处理纹理图像，得到彩色Canny图像及其灰度梯度共生矩阵GGCM，采用能量、梯度平均、灰度平均、相关等二次统计特征参数描述图像的纹理特征，将上述提取纹理特征结合形状特征和SURF特征进行逐层匹配，实现层次匹配下多种特征的融合，弥补单个匹配方法的不足，同时在蕾丝花边库中验证所提检索方法的正确率。实验结果表明，与其他匹配方法相比，该方法提取的纹理特征具有更强的纹理鉴别能力，能较好地实现蕾丝花边检索，有效地提高了检索方法的速...
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| Published in | 计算机工程与科学 Vol. 39; no. 9; pp. 1691 - 1699 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
江南大学教育部针织技术工程研究中心,江苏无锡,214122%江南大学轻工过程先进控制教育部重点实验室,江苏无锡,214122
2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1007-130X |
| DOI | 10.3969/j.issn.1007-130X.2017.09.015 |
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| Abstract | 针对基于图像纹理特征的蕾丝花边检索方法效率低下问题，为提高蕾丝花边检索效率，提出一种基于层次匹配下多种特征融合的蕾丝花边检索方法。通过运用图像纹理特征标识图像，利用Canny算子处理纹理图像，得到彩色Canny图像及其灰度梯度共生矩阵GGCM，采用能量、梯度平均、灰度平均、相关等二次统计特征参数描述图像的纹理特征，将上述提取纹理特征结合形状特征和SURF特征进行逐层匹配，实现层次匹配下多种特征的融合，弥补单个匹配方法的不足，同时在蕾丝花边库中验证所提检索方法的正确率。实验结果表明，与其他匹配方法相比，该方法提取的纹理特征具有更强的纹理鉴别能力，能较好地实现蕾丝花边检索，有效地提高了检索方法的速率和准确率。 |
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| AbstractList | 针对基于图像纹理特征的蕾丝花边检索方法效率低下问题，为提高蕾丝花边检索效率，提出一种基于层次匹配下多种特征融合的蕾丝花边检索方法。通过运用图像纹理特征标识图像，利用Canny算子处理纹理图像，得到彩色Canny图像及其灰度梯度共生矩阵GGCM，采用能量、梯度平均、灰度平均、相关等二次统计特征参数描述图像的纹理特征，将上述提取纹理特征结合形状特征和SURF特征进行逐层匹配，实现层次匹配下多种特征的融合，弥补单个匹配方法的不足，同时在蕾丝花边库中验证所提检索方法的正确率。实验结果表明，与其他匹配方法相比，该方法提取的纹理特征具有更强的纹理鉴别能力，能较好地实现蕾丝花边检索，有效地提高了检索方法的速率和准确率。 TP391.41; 针对基于图像纹理特征的蕾丝花边检索方法效率低下问题,为提高蕾丝花边检索效率,提出一种基于层次匹配下多种特征融合的蕾丝花边检索方法.通过运用图像纹理特征标识图像,利用Canny算子处理纹理图像,得到彩色Canny图像及其灰度梯度共生矩阵GGCM,采用能量、梯度平均、灰度平均、相关等二次统计特征参数描述图像的纹理特征,将上述提取纹理特征结合形状特征和SURF特征进行逐层匹配,实现层次匹配下多种特征的融合,弥补单个匹配方法的不足,同时在蕾丝花边库中验证所提检索方法的正确率.实验结果表明,与其他匹配方法相比,该方法提取的纹理特征具有更强的纹理鉴别能力,能较好地实现蕾丝花边检索,有效地提高了检索方法的速率和准确率. |
| Abstract_FL | Since the efficiency of the lace retrieval method based on image texture features is low,and in order to extract the effective texture features for lace identification,we propose a lace retrieval algorithm containing multiple features fusion through hierarchical matching.Firstly,we process the texture image by the Canny operator and obtain the Canny color image and the gray level-gradient co-occurrence matrix (GGCM).Secondly,energy,average gradient,average grayscale,correlation and other statistical characteristics are used for texture description.Finally,the extracted texture features are matched with geometry features and speeded up robust features (SURF) hierarchically,so the fusion of multiple features under hierarchical matching is realized to compensate for the deficiency of any single matching method and verify the correct rate of the retieval method used in the lace library.Experimental results indicate that the performance of the proposed method is better than other methods,which has a stronger ability of texture identification,and can achieve lace retrieval effectively and improve the reliability and accuracy of image retrieval. |
| Author | 曹霞 李岳阳 罗海驰 蒋高明 丛洪莲 |
| AuthorAffiliation | 江南大学教育部针织技术工程研究中心,江苏无锡214122 江南大学轻工过程先进控制教育部重点实验室,江苏无锡214122 |
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| Author_FL | LI Yue-yang CONG Hong-lian LUO Hai-chi CAO Xia JIANG Gao-ming |
| Author_FL_xml | – sequence: 1 fullname: CAO Xia – sequence: 2 fullname: LI Yue-yang – sequence: 3 fullname: LUO Hai-chi – sequence: 4 fullname: JIANG Gao-ming – sequence: 5 fullname: CONG Hong-lian |
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| ClassificationCodes | TP391.41 |
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| Keywords | 特征融合 gray-level co-occurrence matrix 灰度共生矩阵 灰度梯度共生矩阵 特征匹配 local binary pattern(LBP) speeded up robust feature(SURF) SURF feature fusion gray level-gradient co-occurrence matrix 局部二值模式 hierarchical matching feature matching 层次匹配 |
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| Notes | 43-1258/TP hierarchical matching; feature fusion ; feature matching;gray-level co-occurrence matrix; gray level-gradient co-occurrence matrix ;local binary pattern（LBP） ;speeded up robust feature（SURF） Since the efficiency of the lace retrieval method based on image texture features is low, and in order to extract the effective texture features for lace identification, we propose a lace retrieval algorithm containing multiple features fusion through hierarchical matching. Firstly, we process the texture image by the Canny operator and obtain the Canny color image and the gray level-gradient co-occurrence matrix （GGCM）. Secondly, energy, average gradient, average grayscale, correlation and other statistical characteristics are used for texture description. Finally, the extracted texture features are matched with geometry features and speeded up robust features （SURF） hierarchically, so the fusion of multiple features under hierarchical matching is realized to compensate for the deficiency of any single matching |
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| PublicationYear | 2017 |
| Publisher | 江南大学教育部针织技术工程研究中心,江苏无锡,214122%江南大学轻工过程先进控制教育部重点实验室,江苏无锡,214122 |
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| Snippet | 针对基于图像纹理特征的蕾丝花边检索方法效率低下问题，为提高蕾丝花边检索效率，提出一种基于层次匹配下多种特征融合的蕾丝花边检索方法。通过运用图像纹理特征标识图像，... TP391.41; 针对基于图像纹理特征的蕾丝花边检索方法效率低下问题,为提高蕾丝花边检索效率,提出一种基于层次匹配下多种特征融合的蕾丝花边检索方法.通过运用图像纹理特征标... |
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| SubjectTerms | SURF 局部二值模式 层次匹配 灰度共生矩阵 灰度梯度共生矩阵 特征匹配 特征融合 |
| Title | 基于层次匹配下多种特征融合的蕾丝花边检索方法 |
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