Image retrieval is widely used area for number of applications like journalism, medicine, art collections, scientific database .Most of existing image search engines are text query based where retrieval result is ambiguous due to multiple meanings of provided textual query. So proposed system targets at the retrieving relevant images based on user�s search intention. A novel image retrieval approach uses Text query and Visual information of image for retrieval .Main objective of this system is to capture the user�s search intention in just �One Click� query image and to display most similar images to this clicked image based on its content . Firstly user�s intention is captured by asking user to click one image from result of text based image retrieval .After that clusters of images are formed based on their visual content and visual query hence text query is expanded . Finally Expanded keyword and Visual query expansion are used to retrieve more relevant images. In this paper best combination techniques for important features like Color ,Texture, and shape are used to measure visual similarity between images . Mainly Daubechies� wavelet transform for better frequency resolution and Frei-Chen edge detector which is less sensitive to noise and able to detect edges with small gradients is used .Experimental results shows that our system gives good result by using above combination which is tested on multiple queries and it helps in improving the precision of top-ranked images .

Image reranking, Image search, Intention, Image pool expansion, Keyword expansion, Precison.,Visual features,Visual query expansion

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