Visual rerank: Applying to large scale image search as a soft computing approach

The explosive growth and widespread accessibility of community-contributed media content on the Internet has led to a surge of research activity in visual search. Also, the fast development of internet applications and increasing popularity of modern digital gadgets leads to a very huge collection of image database. However, it remains uncertain whether such techniques will generalize to a large number of popular Web queries and whether the potential improvement to search quality guarantees additional computational cost. Due to the success of text based search of Web pages and in part, to the difficulty and expense of using image based signals, most search engines return images solely based on the text of the pages from which the images are linked. No image analysis takes place to determine relevance/quality. This can yield results of inconsistent quality. So, such kind of visual search approach has proven unsatisfying as it often entirely ignores the visual content itself as a ranking signal. To address this issue, visual reranking, defined as reordering of visual images based on their visual appearance can be used. The major advantages of this approach is that, it improves the search performance.