The new issue 10(4) is online in the ACM Digital Library: Cross-Domain Multi-Event Tracking via CO-PMHT dl.acm.org/citation.cfm?id=2602633 Tianzhu Zhang, Changsheng Xu With the massive growth of events on the Internet, efficient organization and monitoring of events becomes a practical challenge. To deal with this problem, we propose a novel CO-PMHT (CO-Probabilistic Multi-Hypothesis Tracking) algorithm for cross-domain multi-event tracking to obtain their informative summary details and evolutionary trends over time. We collect a large-scale dataset by searching keywords on two domains (Gooogle News and Flickr) and downloading both images and textual content for an event. Given the input data, our algorithm can track multiple events in the two domains collaboratively and boost the tracking performance. Specifically, the bridge between two domains is a semantic posterior probability, that avoids the domain gap. Personalized Video Recommendation through Graph Propagation dl.acm.org/citation.cfm?id=2598779 Qinghua Huang, Bisheng Chen, Jingdong Wang, Tao Mei The rapid growth of the number of videos on the Internet provides enormous potential for users to find content of interest. However, the vast quantity of videos also turns the finding process into a difficult task. In this article, we address the problem of providing personalized video recommendation for users. Rather than only exploring the user-video bipartite graph that is formulated using click information, we first combine the clicks and queries information to build a tripartite graph. In the tripartite graph, the query nodes act as bridges to connect user nodes and video nodes. Then, to further enrich the connections between users and videos, three subgraphs between the same kinds of nodes are added to the tripartite graph by exploring content-based information (video tags and textual queries). Understanding Video Sharing Propagation in Social Networks: Measurement and Analysis dl.acm.org/citation.cfm?id=2594440 Haitao Li, Xu Cheng, Jiangchuan Liu Modern online social networking has drastically changed the information distribution landscape. Recently, video has become one of the most important types of objects spreading among social networking service users. The sheer and ever-increasing data volume, the broader coverage, and the longer access durations of video objects, however, present significantly more challenges than other types of objects. This article takes an initial step toward understanding the unique characteristics of video sharing propagation in social networks. Based on realworld data traces from a large-scale online social network, we examine the user behavior from diverse aspects and identify different types of users involved in video propagation. Bilateral Correspondence Model for Words-and-Pictures Association in Multimedia-Rich Microblogs dl.acm.org/citation.cfm?id=2611388 Zhiyu Wang, Peng Cui, Lexing Xie, Wenwu Zhu, Yong Rui, Shiqiang Yang Nowadays, the amount of multimedia contents in microblogs is growing significantly. More than 20% of microblogs link to a picture or video in certain large systems. The rich semantics in microblogs provides an opportunity to endow images with higher-level semantics beyond object labels. However, this raises new challenges for understanding the association between multimodal multimedia contents in multimedia-rich microblogs. Disobeying the fundamental assumptions of traditional annotation, tagging, and retrieval systems, pictures and words in multimedia-rich microblogs are loosely associated and a correspondence between pictures and words cannot be established. To address the aforementioned challenges, we present the first study analyzing and modeling the associations between multimodal contents in microblog streams, aiming to discover multimodal topics from microblogs by establishing correspondences between pictures and words in microblogs. Fast Near-Duplicate Image Detection Using Uniform Randomized Trees dl.acm.org/citation.cfm?id=2602186 Yanqiang Lei, Guoping Qiu, Ligang Zheng, Jiwu Huang Indexing structure plays an important role in the application of fast near-duplicate image detection, since it can narrow down the search space. In this article, we develop a cluster of uniform randomized trees (URTs) as an efficient indexing structure to perform fast near-duplicate image detection. The main contribution in this article is that we introduce “uniformity” and “randomness” into the indexing construction. The uniformity requires classifying the object images into the same scale subsets. Such a decision makes good use of the two facts in near-duplicate image detection, namely: (1) the number of categories is huge; (2) a single category usually contains only a small number of images. Personalized Photograph Ranking and Selection System Considering Positive and Negative User Feedback dl.acm.org/citation.cfm?id=2584105 Che-Hua Yeh, Brian A. Barsky, Ming Ouhyoung In this article, we propose a novel personalized ranking system for amateur photographs. The proposed framework treats the photograph assessment as a ranking problem and we introduce the idea of personalized ranking, which ranks photographs considering both their aesthetic qualities and personal preferences. Photographs are described using three types of features: photo composition, color and intensity distribution, and personalized features. An aesthetic prediction model is learned from labeled photographs by using the proposed image features and RBF-ListNet learning algorithm. The experimental results show that the proposed framework outperforms in the ranking performance: a Kendalls tau value of 0.432 is significantly higher than those obtained by the features proposed in one of the state-of-the-art approaches (0.365) and by learning based on support vector regression (0.384). Placing Videos on a Semantic Hierarchy for Search Result Navigation dl.acm.org/citation.cfm?id=2578394 Song Tan, Yu-Gang Jiang, Chong-Wah Ngo Organizing video search results in a list view is widely adopted by current commercial search engines, which cannot support efficient browsing for complex search topics that have multiple semantic facets. In this article, we propose to organize video search results in a highly structured way. Specifically, videos are placed on a semantic hierarchy that accurately organizes various facets of a given search topic. To pick the most suitable videos for each node of the hierarchy, we define and utilize three important criteria: relevance, uniqueness, and diversity. Extensive evaluations on a large YouTube video dataset demonstrate the effectiveness of our approach.
Posted on: Fri, 25 Jul 2014 07:35:27 +0000
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