Learn to Personalized Image Search from the Photo Sharing Websites

projects 2012

Learn to Personalized Image Search from the Photo Sharing Websites – projects 2012

Abstract:Learn to Personalized Image Search from the Photo Sharing Websites – projects 2012

Technology Used: Java/ J2EE

Abstract—Increasingly developed social sharing websites, like Flickr and Youtube, allow users to create, share, annotate and comment medias. The large-scale user-generated meta-data not only facilitate users in sharing and organizing multimedia content,but provide useful information to improve media retrieval and management. Personalized search serves as one of such
examples where the web search experience is improved by generating the returned list according to the modified user search intents. In this paper, we exploit the social annotations and propose a novel framework simultaneously considering the user and query relevance to learn to personalized image search. The basic premise is to embed the user preference and query-related
search intent into user-specific topic spaces. Since the users’ original annotation is too sparse for topic modeling, we need to enrich users’ annotation pool before user-specific topic spaces construction. The proposed framework contains two components:

1) A Ranking based Multi-correlation Tensor Factorization model is proposed to perform annotation prediction, which is considered as users’ potential annotations for the images;

2) We introduce User-specific Topic Modeling to map the query relevance and
user preference into the same user-specific topic space. For performance evaluation, two resources involved with users’ social activities are employed. Experiments on a large-scale Flickr dataset demonstrate the effectiveness of the proposed method

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2 Thoughts to “Learn to Personalized Image Search from the Photo Sharing Websites”

  1. pranay

    plz seb\nd coding for this project

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