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Efficient Mean Shift Algorithm based Color Images Categorization and Searching


S. K. Vijay, Sanjay Rathore, Abhishek Verma, Hemraj Singh Thakur


Vol. 10  No. 11  pp. 152-156


Interest in image retrieval has increased in large part due to the rapid growth of the World Wide Web. The explosive growth of digital image collections on the Web sites is calling for an efficient and intelligent method of browsing, searching, and retrieving images. Content-based image retrieval, a technique which uses visual contents to search images from large scale image databases according to user’s interests. In our approach, the color features are extracted using the mean shift algorithm, a robust clustering technique, which does not require prior knowledge of the number of clusters, and does not constrain the shape of the clusters. Dominant objects are obtained by performing region grouping of segmented thumbnails. The category for an image is generated automatically by analyzing the image for the presence of a dominant object. Categories described here are of statistical and syntactical descriptions rather than semantically. The images in the database are clustered based on region feature similarity using Euclidian distance. Placing an image into a category can help the user to navigate retrieval results more effectively. Extensive experimental results illustrate excellent performance.


Content-based, Segmentation, Category, Indexing