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Survey on Web-Scale Image Search and Re-Ranking With Semantic Signatures.

Darshana C. Chaudhari, Priti Subramanium.

Web-scale image search results can be improved by using image re-ranking technique. Many commercial search engines such as Google, Yahoo and Bing have been adopted this strategy. Firstly user is given a query keyword; text-based image retrieval is done. Then user is asked to select a query image from pool with minimum effort just by one click and images from a pool extracted by text-based information are re-ranked based on visual resemblance with the query image. There are some challenges in this method. Visual feature vectors need to be short to achieve high matching efficiency. But some of famous visual features are large in size. Another challenge is that the resemblance of visual features may not well associate with images’ high-level semantic meanings. For overcoming this problem, in this paper, a new technique is proposed for web-scale image re-ranking. As an alternative to manually defining an entire concept glossary, different semantic spaces for different query keywords can be found offline independently and automatically. Semantic signatures of the images can be acquired by projecting their visual features into their related semantic spaces and these semantic signatures can be computed using Hashing techniques. At the online stage, these compacted semantic signatures of images are to be compared to re-rank images. It significantly improves the efficiency and accuracy of web-image search and re-ranking.