The existing image retrieval process is based on text-based approach where the input to the search engine is given as the text. Typically, in the development of an image requisition system, semantic image retrieval relies heavily on the related captions, e.g., file-names, categories, annotated keywords, and other manual descriptions. Unfortunately, this kind of textual-based image retrieval always suffers from two problems: high-priced manual annotation and inappropriate automated annotation. To overcome this drawback the proposed work is defined based on CBIR system. The mission of this work is to present an image conceptually, with a set of low-level visual features such as color, texture, and shape and retrieve similar kind of images from the database based on features extracted from the query image. Input to the search engine is given as “Image” itself and this method serves to visually represent the query. Subsequently based on the multi-feature image extraction, Rerankers are constructed which are used to rank the top N output based on the query image. This ensures high accuracy and reliability with less noise in the displayed result.