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A Novel Content Based Image Retrieval Method for Large Image Dataset

机译:一种新的基于内容的大图像数据集图像检索方法

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In numerous application domains like medical, education, crime hindrance, geography, commerce, and biomedicine, the quantity of digital info is growing quickly. The problem seems once retrieving the knowledge from the storage media. Content-based image retrieval systems aim to retrieve images from massive image databases almost like the question image supported the similarity between image options. During this work we tend to tend to gift a CBIR system that uses the color feature as a visible feature to represent the images. The information contains color images, thus we tend to use the RGB color area to represent the images. We tend to use Diagonal Mean, histogram Analysis, R G B parts and Retrieving similar images exploitation Euclidean Distance. For the ensuing images we tend to extract the color feature by counting the precision. We tend to compared with alternative existing systems that use identical options to represent the images. We tend to represent higher performance of our system against the opposite systems.
机译:在医疗,教育,犯罪预防,地理,商业和生物医学等众多应用领域中,数字信息的数量正在迅速增长。问题似乎一度从存储介质中检索到。基于内容的图像检索系统旨在从海量图像数据库中检索图像,就像问题图像支持图像选项之间的相似性一样。在这项工作中,我们倾向于使用CBIR系统,该系统使用颜色特征作为可见特征来表示图像。该信息包含彩色图像,因此我们倾向于使用RGB颜色区域来表示图像。我们倾向于使用对角均值,直方图分析,RG B部分和利用欧几里德距离检索相似图像。对于随后的图像,我们倾向于通过计算精度来提取颜色特征。我们倾向于将其与使用相同选项来表示图像的现有替代系统进行比较。我们倾向于代表相对于相反系统的更高性能。

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