Image Based Personalized Search from the Picture Sharing Websites

Although search has become a popular feature in many search engines, including Yahoo!, MSN, Google, etc., the majority of image searches use very little, if any, image information. 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.Web search engines help users find useful information on the World Wide Web (WWW). However, when the same query is submitted by different users, typical search engines return the same result regardless of who submitted the query. Users are increasingly pursuing complex task oriented goals on the Web, such as making travel arrangements, managing finances or planning purchases. Searchers create and use external records of their actions and the corresponding results by writing/ typing notes, using copy and paste functions, and making printouts. The social media sites, such as Flickr and del.icio.us, allow users to upload content and annotate it with descriptive labels known as tags, join special-interest groups, etc. We believe user-generated metadata expresses user’s tastes and interests and can be used to modified information to an individual user. Specifically, we describe a machine learning method that analyzes a corpus of tagged content to find hidden topics. We then these learned topics to select content that matches user’s interests. We empirically validated this approach on the social picture-allocation site Flickr, which allows users to annotate icon s with freely chosen tags and to search for icon s labeled with a certain tag. We use metadata associated with icon s tagged with an ambiguous query term to identify topics corresponding to different senses of the term, and then modified results of icon search by displaying to the user only those icon s that are of interest to her.