Focused image search in the social Web.

Recently, social multimedia-sharing websites, which allow users to upload, annotate, and share online photo or video collections, have become increasingly popular. The user tags or annotations constitute the new multimedia meta-data . We present an image search system that exploits both image textual and visual information. First, we use focused crawling and DOM Tree based web data extraction methods to extract image textual features from social networking image collections. Second, we propose the concept of visual words to handle the image\u27s visual content for fast indexing and searching. We also develop several user friendly search options to allow users to query the index using words and image feature descriptions (visual words). The developed image search system tries to bridge the gap between the scalable industrial image search engines, which are based on keyword search, and the slower content based image retrieval systems developed mostly in the academic field and designed to search based on image content only. We have implemented a working prototype by crawling and indexing over 16,056 images from flickr.com, one of the most popular image sharing websites. Our experimental results on a working prototype confirm the efficiency and effectiveness of the methods, that we proposed

Hybrid Query Session and Content-Based Recommendations for Enhanced Search

This paper presents a simple and intuitive method for mining search engine query logs to get fast query recommendations on a large scale industrial-strength search engine. In order to get a comprehensive solution, we combine two methods together. First, we study and model search engine users' sequential search behavior, and interpret this consecutive search behavior as client-side query refinement, that should form the basis for the search engine's own query refinement process. This query refinement process is exploited to learn useful relations and build fuzzy associative memories that help generate related queries via a fuzzy inference process. Second, we combine this method with a traditional content based similarity method to compensate for the high sparsity of real query log data, and more specifically, the shortness of most query sessions