Tag Archives: social

Image Reranking based on Topic Diversity


Social media sharing websites allow users to annotate images with free tags, which significantly contribute to the development of the web image retrieval. Tag-based image search is an important method to find images shared by users in social networks. However, how to make the top ranked result relevant and with diversity is challenging. In this paper, we propose a topic diverse ranking approach for tag-based image retrieval with the consideration of promoting the topic coverage performance. First, we construct a tag graph based on the similarity between each tag. Then community detection method is conducted to mine the topic community of each tag. After that, inter-community and intra-community ranking are introduced to obtain the final retrieved results. In the inter-community ranking process, an adaptive random walk model is employed to rank the community based on the multi-information of each topic community. Besides, we build an inverted index structure for images to accelerate the searching process. Experimental results on Flickr dataset and NUS-Wide datasets show the effectiveness of the proposed approach.


Image Re-ranking based on Topic Diversity”,  IEEE Transactions on Image Processing, 2017

Unique ID -SBI1085


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Automatic Generation of Social Event Storyboard from Image Click-through Data

Introduction :

Recent studies have shown that a noticeable percentage of web search traffic is about social events. While traditional websites can only show human-edited events, in this paper we present a novel system to automatically detect events from search log data and generate storyboards where the events are arranged chronologically. We chose image search log as the resource for event mining, as search logs can directly reflect people’s interests. To discover events from log data, we present a Smooth Nonnegative Matrix Factorization framework (SNMF) which combines the information of query semantics, temporal correlations, search logs and time continuity. Moreover, we consider the time factor an important element since different events will develop in different time tendencies. In addition, to provide a media-rich and visually appealing storyboard, each event is associated with a set of representative photos arranged along a timeline. These relevant photos are automatically selected from image search results by analyzing image content features. We use celebrities as our test domain, which takes a large percentage of image search traffics. Experiments consisting of web search traffic on 200 celebrities, for a period of six months, show very encouraging results compared with handcrafted editorial storyboards.

Reference IEEE paper:

Automatic Generation of Social Event Storyboard from Image Click-through Data”, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2017.

Unique ID -SBI1084

DomainImage Processing

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Social Q&A: An Online Social Network Based Question and Answer System

Introduction :

Question and Answer (Q&A) systems play a vital role in our daily life for information and knowledge sharing. Users post questions and pick questions to answer in the system. Due to the rapidly growing user population and the number of questions, it is unlikely for a user to stumble upon a question by chance that (s)he can answer. Also, altruism does not encourage all users to provide answers, not to mention high quality answers with a short answer wait time. The primary objective of this paper is to improve the performance of Q&A systems by actively forwarding questions to users who are capable and willing to answer the questions. To this end, we have designed and implemented Social Q&A, an online social network based Q&A system. Social Q&A leverages the social network properties of common-interest and mutual-trust friend relationship to identify an asker through friendship who are most likely to answer the question, and enhance the user security. We also improve Social Q&A with security and efficiency enhancements by protecting user privacy and identifies, and retrieving answers automatically for recurrent questions. We describe the architecture and algorithms, and conducted comprehensive large-scale simulation to evaluate Social Q&A in comparison with other methods. Our results suggest that social networks can be leveraged to improve the answer quality and asker’s waiting time. We also implemented a real prototype of Social Q&A, and analyze the Q&A behavior of real users and questions from a small-scale real-world Social Q&A system.

Reference IEEE paper :

“SocialQ&A: An Online Social Network Based Question and Answer System”, IEEE Transactions on Big Data, 2017.

Unique ID – SBI1083


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