A Novel Architecture for Personalized Image Retrieval from Social Websites
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Abstract
The social media photo sharing sites allows users to upload their photos, annotate them with tags, submit them to groups, and also to form
social networks by adding other users as contacts. The site offers multiple ways of browsing or searching it. One option is tag search, which returns
all images tagged with a specific keyword. If the keyword is ambiguous, e.g. jaguar could mean an animal or a car, tag search results will include
many images that are not relevant to the sense the user had in mind when executing the query. This paper describes an approach to personalized
image indexing and retrieval. To tackle the issue of subjectivity in Content-Based Image Retrieval (CBIR), users can extend their own tag vocabulary
and make the system learn it. This tag extension concept demonstrates that content based image search can be efficiently adapted to user interests and
matches personalized image retrieval.
Keywords: Sharing, Search, Upload, Photos, Tagging.
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