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Comparison of Color and Color with Edge Feature Extraction Using Contribution-Based Clustering Algorithm

机译:使用基于贡献的聚类算法对颜色和颜色进行边缘特征提取的比较

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Search and retrieval of images based on content has attracted considerable attention in recent years from the research community. Classification and Clustering algorithm are used to improve the result of Content based Image retrieval. This paper relies on a combination of color and edge features of image for the accurate retrieval of images. Color features are extracted by RGB color histogram and edge features are extracted by using canny edge detection algorithm. Contribution based Clustering algorithm is applied to those features to form the cluster of images. Experimental results have been tested on the test dataset of about 771 images from the Washington University database. Combination of Color with Edge features gives the better result than the standalone color extraction with contribution based clustering algorithm. Our experiment improves the recall value and f-measure value of image retrieval.
机译:近年来,基于内容的图像搜索和检索已引起研究界的广泛关注。分类和聚类算法用于提高基于内容的图像检索的结果。本文依靠图像颜色和边缘特征的组合来精确检索图像。通过RGB颜色直方图提取颜色特征,并使用Canny边缘检测算法提取边缘特征。基于贡献的聚类算法被应用于那些特征以形成图像的聚类。实验结果已经在华盛顿大学数据库中约771张图像的测试数据集中进行了测试。将颜色与边缘特征相结合所产生的效果要优于具有基于贡献的聚类算法的独立颜色提取。我们的实验提高了图像检索的查全率和f测度值。

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