An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance Feedback

Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similaritybetween a query image and the image database. Image contents are plays significant role for imageretrieval. There are three fundamental bases for content-based image retrieval, i.e. visual featureextraction, multidimensional indexing, and retrieval system design. Each image has three contents such as:color, texture and shape features. Color and texture both plays important image visual features used inContent-Based Image Retrieval to improve results. Color histogram and texture features have potential toretrieve similar images on the basis of their properties. As the feature extracted from a query is low level, itis extremely difficult for user to provide an appropriate example in based query. To overcome theseproblems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous forprovide promising solution.