宋冬慧, 马晓楠, 王冲. 基于SVM改进的图像反馈检索方法研究[J]. 微电子学与计算机, 2018, 35(4): 79-83, 88.
引用本文: 宋冬慧, 马晓楠, 王冲. 基于SVM改进的图像反馈检索方法研究[J]. 微电子学与计算机, 2018, 35(4): 79-83, 88.
SONG Dong-hui, MA Xiao-nan, WANG Chong. Research on Image Feedback Retrieval Method Based on Improving SVM[J]. Microelectronics & Computer, 2018, 35(4): 79-83, 88.
Citation: SONG Dong-hui, MA Xiao-nan, WANG Chong. Research on Image Feedback Retrieval Method Based on Improving SVM[J]. Microelectronics & Computer, 2018, 35(4): 79-83, 88.

基于SVM改进的图像反馈检索方法研究

Research on Image Feedback Retrieval Method Based on Improving SVM

  • 摘要: 基于内容的图像检索系统在用SVM算法进行反馈时, 为了提高图像检索的精度, 减少用户的反馈次数, 提出了KRSVM算法.该算法通过运用颜色矩、颜色直方图和Hu矩提取图像的颜色和形状特征, 并用K-means算法对特征进行多次聚类, 同时使用SVM算法对其图像样本进行训练时, 引入了多个SVM核函数和Relief算法, 对核函数和图像特征进行权重分配.相比较传统的SVM算法和K-SVM算法, 本算法在一定程度上提高了图像检索的准确度和用户满意度.

     

    Abstract: The feedback of the content-based image retrieval system with the SVM algorithm, In order to improve the accuracy of image retrieval and reduce the number of user feedback, we proposed a novel algorithm called KRSVM, extracting features of the image about color shape and shape by using color moments, color histograms and Hu moments, and they are clustered with the multiple clusters of the K-means algorithm, at the same time, the SVM algorithm trains the image samples using a number of SVM kernel functions and Relief algorithm, they are for the weight distribution of the kernel functions and the image features. Compared with the traditional SVM algorithm and K-SVM algorithm, this algorithm is more better in terms of accuracy and requirements to some extent.

     

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