Hashing based re-ranking of web images using query-specific semantic signatures

Nowadays online image search become more essential. In this paper, we have extended existing system for image re-ranking is explained. The existing system is divided into offline and online parts. In offline part various semantic spaces are automatically learns for different query keywords. Image Semantic content as signatures are generated by mapping the image features i.e. visual features into its semantic spaces related to image context. In online stage, semantic signatures computed from the different semantic space mentioned by the query keyword are equated with semantic signatures of query image for image re-ranking. We are extended the current frame work by adding new technique of hashing. Semantic signatures are small in dimensions, it is possible to make it more compressed and with use of hashing technologies it further enhance their matching efficiency. In this we use locality sensitive hashing concept based on nearest neighbor algorithms. To find more similar item in d-dimensional space, these algorithms are already been applied in different practical scenarios. In this, we implemented a recently discovered hashing-based algorithm to improve the online matching effectiveness of image re-ranking system, for the case the images are represented as objects as points in the rf-dimensional Euclidean space. The locality sensitive hashing algorithm produces the output which is optimal near in the class of nearest neighbor algorithms. The online matching efficiency is improved by using the hashing technique as compare to existing search methods. With the use of hashing technique the system performance is improved by 38%.