The image retrieval applications are designed to fetch required images from the image databases. Images are searched using textual query or images. The textual query based retrieval is performed with image annotations. The image features are used in the content based image retrieval process on the image database provides huge collection of images. Query image features are compared with the database image features. The similarity measures are used to select relevant images from the databases. Relevance feedbacks are collected from the users at the time of query processing. The feedbacks are maintained under the image database and used in subsequent image retrievals. The navigation pattern based relevance feedback model is limited with accuracy and scalability factors. So the content based image retrieval scheme is enhanced to perform image retrieval in a distributed parallel manner and clustering techniques are used to improve the speed and accuracy of image retrieval. This is achieved through a multiscale approach. A quantitative measure is suggested for segmentation evaluation. The goal is to impute the missing data in the presence of edges or boundaries and recover the image. Their performance is compared with another method that imputes the missing values using edge-preserving spatial smoothers with locally varying weights. The proposed system first detects onset and offsets, and then generates segments by matching corresponding onset images.

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Image-mining navigation Pattern Mining Cluster Analysis Edge Detection Object Recognition Relevance Feedback