Enhancing Image Retrieval and Re-ranking Efficiency using Hybrid approach

Many Image search engines are used to share images on social media websites on different occasion. It became very essential for user to retrieve more relevant image from web as per user intention, To search and retrieve images from web which are more relevant to user requirement we have proposed a new system using user intention and hybrid features of images to retrieve and re-rank images to user so that user can use these images for his purpose. A novel data fusion technique to support text-based and content based image recovery combining different heterogeneous features is proposed. The user need to provide just a single tick on an inquiry image and pictures recovered by content based pursuit are re-positioned dependent on their visual and texture similitudes to the enquiry image. Word-based and pictorial expansions are incorporated to detect user intention without additional human feedback. Expanded keywords helps in extending positive model images and furthermore develop the image pool to include additional related images. A lot of visual features which are both efficient and effective for image search are chosen. The n-dimensional feature vector for both colour and texture is reduced to single dimension each, used for comparing the similarity with query image using suitable distance metrics. Further only the images retrieved as a outcome of text created pursuit and image re-position process are compared during run time for finding the similar images; not the entire database. This considerably reduces the computational complexity and improves the search efficiency. With improved feature extraction capturing textual and visual similarities, the proposed one click image search framework gives a productive robotized recovery of comparable images giving promising results with improvement in retrieval efficiency.