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Improved CBIR system using Edge Histogram Descriptor (EHD) and Support Vector Machine (SVM)

机译:使用边缘直方图描述符(EHD)和支持向量机(SVM)的改进的CBIR系统

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摘要

Due to widespread applications found in many areas, Content Based Image Retrieval (CBIR) system is attracting attention of many researchers. Effectiveness of any CBIR system depends on the features extracted to represent an image. So feature extraction is the crucial step in design and development of any Content Based Image Retrieval system. Most commonly used features to represent images are Color, texture and shape. Recently developed CBIR system combines these features to effectively represent an image. This paper first discusses the concept and scope of content based image retrieval system. It also includes the overview of MPEG-7 edge histogram descriptor (EDH) to extract the contents from images. Further it gives the idea of Support Vector Machine (SVM) classifier. In this paper the basic CBIR system is developed by combining features like color moments, color-correlogram and Gabor texture features along with edge histogram descriptor. Further the results obtained are compared with CBIR system using SVM classifier.
机译:由于在许多领域都有广泛的应用,基于内容的图像检索(CBIR)系统吸引了许多研究人员的注意。任何CBIR系统的有效性都取决于提取的代表图像的特征。因此,特征提取是任何基于内容的图像检索系统设计和开发中的关键步骤。代表图像的最常用功能是颜色,纹理和形状。最近开发的CBIR系统结合了这些功能,可以有效地表示图像。本文首先讨论了基于内容的图像检索系统的概念和范围。它还概述了MPEG-7边缘直方图描述符(EDH),以从图像中提取内容。进一步给出了支持向量机(SVM)分类器的思想。在本文中,基本CBIR系统是通过将诸如色矩,色相关图和Gabor纹理特征以及边缘直方图描述符之类的特征相结合而开发的。进一步将获得的结果与使用SVM分类器的CBIR系统进行比较。

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