21-03-2013, 07:15 AM
sir, i need project code in java for above title... can u help me plz
21-03-2013, 07:15 AM
sir, i need project code in java for above title... can u help me plz
21-03-2013, 09:26 AM
To get full information or details of LEARN TO PERSONALIZED IMAGE SEARCH FROM THE PHOTO SHARING WEBSITES please have a look on the pages
https://seminarproject.net/Thread-learn-...g-websites https://seminarproject.net/Thread-learn-...ull-report https://seminarproject.net/Thread-learn-...es-details if you again feel trouble on LEARN TO PERSONALIZED IMAGE SEARCH FROM THE PHOTO SHARING WEBSITES please reply in that page and ask specific fields in LEARN TO PERSONALIZED IMAGE SEARCH FROM THE PHOTO SHARING WEBSITES
03-05-2013, 08:45 PM
I need the ppt for the topic:
Learn to Personalized Image Search from the Photo Sharing Websites
04-05-2013, 09:31 AM
To get full information or details of LEARN TO PERSONALIZED IMAGE SEARCH FROM THE PHOTO SHARING WEBSITES please have a look on the pages
https://seminarproject.net/Thread-learn-...ull-report https://seminarproject.net/Thread-learn-...es-details https://seminarproject.net/Thread-learn-...g-websites if you again feel trouble on LEARN TO PERSONALIZED IMAGE SEARCH FROM THE PHOTO SHARING WEBSITES please reply in that page and ask specific fields
22-06-2013, 12:33 PM
Learn to Personalized Image Search from the Photo Sharing Websites Learn to Personalized.pdf (Size: 3.66 MB / Downloads: 52) 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. INTRODUCTION Keyword-based search has been the most popular search paradigm in today’s search market. Despite simplicity and efficiency, the performance of keyword-based search is far from satisfying. Investigation has indicated its poor user experience - on Google search, for 52% of 20,000 queries, searchers did not find any relevant results [1]. Result Processing can be further classified into result filtering and re-ranking. Result filtering aims to filter irrelevant results that are not of interest to a particular user [27]. While, result re-ranking focuses on re-ordering the results by the degree of users’ preferences estimated. Since our work falls into this category, we mainly review the related work on result re-ranking. Chirita et al. [17] conducted an early work by reranking the search results according to the cosine distance between each URL and user interest profiles constructed. Qiu et al. [21] extended Topic-Sensitive PageRank by incorporating users’ preference vectors. RELATED WORK In recent years, extensive efforts have been focusing on personalized search. Regarding the resources they leveraged, explicit user profile [17], relevance feedback [18], user history data (browsing log [19], click-through data [20], [21] and social annotations [11], [8], [4] etc.), context information [23] (time, location, etc.) and social network [1], [3], [16] are exploited. For the implementation there are two primary strategies [24], query refinement and result processing. In the following we review the related work by the strategy they used. Multi-correlation Smoothness Constraints Photo sharing websites differentiate from other social tagging systems by its characteristic of self-tagging: most images are only tagged by their owners. Fig.4(a) shows the #tagger statistics for Flickr and the webpage tagging system Del.icio.us. We can see that in Flickr, 90% images have no more than 4 taggers and the average number of tagger for each image is about 1.9. However, the average tagger for each webpage in Del.icio.us is 6.1. The severe sparsity problem calls for external resources to enable information propagation. EXPERIMENTS A. Dataset We perform the experiments on a large-scale web image dataset, NUS-WIDE [33]. It contains 269,648 images with 5,018 unique tags collected from Flickr. We crawled the images’ owner information and obtained owner user ID of 247,849 images.10. The collected images belong to 50,120 unique users. Fig.4 shows the distributions of #tagger and #tagged images11 CONCLUSION AND FUTURE WORK How to effectively utilize the rich user metadata in the social sharing websites for personalized search is challenging as well as significant. In this paper we propose a novel framework to exploit the users’ social activities for personalized image search, such as annotations and the participation of interest groups. The query relevance and user preference are simultaneously integrated into the final rank list. Experiments on a large-scale Flickr dataset show that the proposed framework greatly outperforms the baseline. |
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