期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2015
卷号：4
期号：6
页码：12735-12739
出版社：IJECS
摘要：In modern sciences and technologies, images gain much broader scopes due to the ever growingimportance of scientific visualization. The search for similar images in large-scale image databases has beenan active research area in the last couple of years. A very promising approach is content based imageretrieval (CBIR). Content-Based Image Retrieval (CBIR) is a technique that uses image visual features suchas color, texture, shape etc. to retrieve the images from set of large image database according to user’srequest which is in the form of query image. The combination of Color and Texture information have beenthe most important property of any image and provides robust feature set for image retrieval. Theinformation gained by feature extraction is used to measure the similarity between two images. For thecomparison of query image and the database image, similarity measures such as Euclidean Distance, JeffreyDivergence, color histogram matching etc. are used. In this paper, we have used the enhanced entropyfeature which works best for textures with small variances and thus improving retrieval results from existingentropies.
关键词：content based image retrieval; color;feature; texture feature; entropy
