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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)改进CBIR系统

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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:由于许多领域发现的广泛应用程序,基于内容的图像检索(CBIR)系统引起了许多研究人员的关注。任何CBIR系统的有效性取决于提取以表示图像的特征。所以特征提取是基于内容的图像检索系统的设计和开发的关键步骤。最常用的特征来表示图像是颜色,纹理和形状。最近开发的CBIR系统结合了这些特征来有效代表图像。本文首先讨论了基于内容的图像检索系统的概念和范围。它还包括MPEG-7边缘直方图描述符(EDH)的概述,以从图像中提取内容。此外,它给出了支持向量机(SVM)分类器的想法。在本文中,基本的CBIR系统是通过组合颜色矩,颜色相关图和Gabor纹理特征等特征以及边缘直方图描述符而开发的。此外,使用SVM分类器将获得的结果与CBIR系统进行比较。

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