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作 者:谷迁 袁理 杨亚莉[1] 刘军平 GU Qian;YUAN Li;YANG Yali;LIU Junping(School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan, Hubei 430200, China;Hubei Functional Fiber Processing and Testing Engineering Technology Research Center, Wuhan Textile University,Wuhan, Hubei 430200, China;School of Mathematics and Computer, Wuhan Textile University, Wuhan, Hubei 430200, China)
机构地区:[1]武汉纺织大学电子与电气工程学院,湖北武汉430200 [2]武汉纺织大学湖北省功能纤维加工及检测工程技术研究中心,湖北武汉430200 [3]武汉纺织大学数学与计算机学院,湖北武汉430200
出 处:《纺织学报》2022年第3期89-94,共6页Journal of Textile Research
基 金:湖北省自然科学基金项目(2014CFB754);湖北省教育厅科学技术研究计划青年人才项目(Q20141607);中国纺织工业联合会科技指导性计划项目(2018035,2014072);湖北省教育厅科学研究计划指导性项目(B2021106)。
摘 要:为提高色纺织物图像检索系统的有效性与执行效率,建立了融合高级语义特征的色纺织物图像检索系统。该系统利用深度卷积神经网络对色纺织物图像的风格等高级语义特征进行提取,并融合图像的局部二值模式与方向梯度直方图等浅层纹理特征构建层次化检索系统。与此同时,采用二进制哈希编码对提取的高维特征向量进行降维。采用该系统对具有9种不同纹理风格的色纺织物样本图像进行检索,结果表明,该系统的Top-10查全率与平均准确率分别达到了97.37%和87.54%,具有理想的有效性与鲁棒性;相较于直接利用高级语义特征进行检索,提出的检索方法执行效率提升约750倍。In order to improve the effectiveness and execution efficiency of image retrieval systems for colored spun fabrics,an image retrieval system based on low-order features and high-level semantic features was established.The system uses the deep convolution neural network to extract the style semantic features of colored spun fabrics,and then constructs a hierarchical retrieval system by fusing low-order texture features such as local binary pattern and directional gradient histogram.Binary hash coding was used to reduce the dimension of the extracted high-dimensional feature vector.The results showed that for the retrieval of the 9 sample images of colored spun fabrics with different texture styles,the Top-10 recall and average accuracy of the system reached 97.37%and 87.54%,respectively,indicating good effectiveness and robustness.Compared with the direct use of advanced semantic features,the retrieval time of the proposed method is about 750 times faster.
关 键 词:色纺织物 图像检索 高级语义特征 二进制哈希码 层次化特征
分 类 号:TS101.9[轻工技术与工程—纺织工程]
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