Abstract
Content-Based Image Retrieval is commonly utilized in most of the systems. Based on image content, CBIR extracts images that are relevant to the given query image from large image databases. Most of the CBIR systems available in the literature extract only concise feature sets that limit the retrieval efficiency. In this paper, extensive feature such as shape is extracted from the database images and stored in the feature library. For this shape, Improved Hill Climbing Based Segmentation (IHCBS) technique is used. When a query image is given, the features are extracted in the similar fashion. Subsequently, Similarity measure is performed between the query image features and the database image features. Hence, from the KLD-based similarity measure, the database images that are relevant to the given query image are retrieved. The proposed CBIR technique is evaluated by querying different images and the retrieval efficiency is evaluated by determining precision-recall values for the retrieval results.
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