Bridging the Semantic Gap in Image Search by Integrating Visual and Textual Features

In today's Development of internet online searching has been become more popular. To provide easy, efficient and precise retrieval of images that satisfies users need. Nowadays, highly accessible searching engine is “Google”. In Google engine, we can search either textual query or image. Sometimes, Google provides irrelevant results which are not useful for users need. In this paper, we present image search by integrating visual and textual features to improve retrieval performance. It consists of major modules like indexing and ranking in which we are using template matching and relative pixel to pixel matching algorithm. Experimental results show that our approach improves precision by giving both the input textual and image simultaneously.