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International Conference on Big Data and Smart Computing (BigComp)

作者： Nguyen Anh Tu Cho, Jinsung Lee, Young-Koo Kyung Hee Univ Dept Comp Engn Seoul South Korea

ISBN: (纸本)9781467387965

Social image search becomes an active research field in recent years due to the rapid development in big data processing technologies. In the retrieval systems, text description/tags play a key role to bridge the semantic gap between low-level features and higher-level concepts, and so guarantee the reliable search. However, in practice manual tags are usually noisy and incomplete, resulting in a limited performance of image retrieval. To tackle this problem, we propose a probabilistic topic model to formalize the correlation of tags with visual features via the latent semantic topics. Our proposed approach allows us to effectively annotate and refine tags based on a Monte Carlo Markov Chain algorithm for approximate inference. Moreover, we present a measuring scheme using the refined tags and extracted topics for ranking the images. The experimental results from two large benchmark datasets show that our approach provides promising accuracy.

关键词： Topic modeling probabilistic graphical model tag refinement image annotation image retrieval

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probabilistic modeling of Imaging, Genetics and Diagnosis

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2016年第7期35卷 1765-1779页

作者： Batmanghelich, Nematollah K. Dalca, Adrian Quon, Gerald Sabuncu, Mert Golland, Polina MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Univ Calif Davis Davis CA 95616 USA Massachusetts Gen Hosp Martinos Ctr Biomed Imaging Charlestown MA 02129 USA

We propose a unified Bayesian framework for detecting genetic variants associated with disease by exploiting image-based features as an intermediate phenotype. The use of imaging data for examining genetic associations promises new directions of analysis, but currently the most widely used methods make sub-optimal use of the richness that these data types can offer. Currently, image features are most commonly selected based on their relevance to the disease phenotype. Then, in a separate step, a set of genetic variants is identified to explain the selected features. In contrast, our method performs these tasks simultaneously in order to jointly exploit information in both data types. The analysis yields probabilistic measures of clinical relevance for both imaging and genetic markers. We derive an efficient approximate inference algorithm that handles the high dimensionality of image and genetic data. We evaluate the algorithm on synthetic data and demonstrate that it outperforms traditional models. We also illustrate our method on Alzheimer's Disease Neuroimaging Initiative data.

关键词： Bayesian models imaging genetics probabilistic graphical model variational inference

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A Dual-Field Sensing Scheme for a Guidance System for the Blind

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SENSORS 2016年第5期16卷 667-667页

作者： Lin, Qing Han, Youngjoon Soongsil Univ Sports IT Convergence Dept 511 Sangdo Dong Seoul 156743 South Korea Soongsil Univ Dept Smart Syst Software 511 Sangdo Dong Seoul 156743 South Korea

An electronic guidance system is very helpful in improving blind people's perceptions in a local environment. In our previous work "Lin, Q.;Han, Y. A Context-Aware-Based Audio Guidance System for Blind People Using a Multimodal Profile model. Sensors 2014, 14, 18670-18700", a context-aware guidance system using a combination of a laser scanner and a camera was proposed. By using a near-field graphical model, the proposed system could interpret a near-field scene in very high resolution. In this paper, our work is extended by adding a far-field graphical model. The integration of the near-field and the far-field models constitutes a dual-field sensing scheme. In the near-field range, reliable inference of the ground and object status is obtained by fusing range data and image data using the near-field graphical model. In the far-field range, which only the camera can cover, the far-field graphical model is proposed to interpret far-field image data based on appearance and spatial prototypes built using the near-field interpreted data. The dual-field sensing scheme provides a solution for the guidance systems to optimise their scene interpretation capability using simple sensor configurations. Experiments under various local conditions were conducted to show the efficiency of the proposed scheme in improving blind people's perceptions in urban environments.

关键词： electronic travel aids guidance systems for blind people sensor fusion scene interpretation probabilistic graphical model context awareness

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Tag Correspondence model for User Tag Suggestion

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Journal of Computer Science & Technology 2015年第5期30卷 1063-1072页

作者：涂存超刘知远孙茂松 Department of Computer Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory on Intelligent Technology and Systems Tsinghua University Beijing 100084 China National Laboratory for Information Science and Technology Tsinghua University Beijing 100084 China Jiangsu Collaborative Innovation Center for Language Ability Jiangsu Normal University Xuzhou 221009 China

Some microblog services encourage users to annotate themselves with multiple tags, indicating their attributes and interests. User tags play an important role for personalized recommendation and information retrieval. In order to better understand the semantics of user tags, we propose Tag Correspondence model （TCM） to identify complex correspondences of tags from the rich context of microblog users. The correspondence of a tag is referred to as a unique element in the context which is semantically correlated with this tag. In TCM, we divide the context of a microblog user into various sources （such as short messages, user profile, and neighbors）. With a collection of users with annotated tags, TCM can automatically learn the correspondences of user tags from multiple sources. With the learned correspondences, we are able to interpret implicit semantics of tags. Moreover, for the users who have not annotated any tags, TCM can suggest tags according to users＇ context information. Extensive experiments on a real-world dataset demonstrate that our method can efficiently identify correspondences of tags, which may eventually represent semantic meanings of tags.

关键词： microblog user tag suggestion tag correspondence model probabilistic graphical model context

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Search engine reinforced semi-supervised classification and graph-based summarization of microblogs

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NEUROCOMPUTING 2015年 152卷 274-286页

作者： Chen, Yan Zhang, Xiaoming Li, Zhoujun Ng, Jun-Ping Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China Bloomberg LP New York NY USA

There is an abundance of information found on rnicroblog services due to their popularity. However the potential of this trove of information is limited by the lack of effective means for users to browse and interpret the numerous messages found on these services. We tackle this problem using a two-step process, first by slicing up the search results of current retrieval systems along multiple possible genres. Then, a summary is generated from the microblog messages attributed to each genre. We believe that this helps users to better understand the possible interpretations of the retrieved results and aid them in finding the information that they need. Our novel approach makes use of automatically acquired information from external search engines in each of these two steps. We first integrate this information with a semi-supervised probabilistic graphical model, and show that this helps us to achieve significantly better classification performance without the need for much training data. Next we incorporate the extra information into graph-based summarization, and demonstrate that superior summaries (up to 30% improvement in ROUGE-1) are obtained. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Microblog Topic classification Summarization probabilistic graphical model Semi-supervised Pagerank

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Identifying Physical Interactions from Climate Data: Challenges and Opportunities

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COMPUTING IN SCIENCE & ENGINEERING 2015年第6期17卷 27-34页

作者： Ebert-Uphoff, Imme Deng, Yi Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA Georgia Inst Technol Sch Earth & Atmospher Sci Atlanta GA 30332 USA

Recent research has shown the potential of using probabilistic graphical models to identify and visualize interactions in the Earth's climate system. Studying the resulting pathways is of great interest to scientists because it helps them learn subtle details about the underlying dynamical mechanisms governing our planet's climate.

关键词： climatology data analysis geophysics computing atmosphere climate data physical interaction identification Atmospheric modeling Computational modeling Data models graphical models Mathematical model Meteorology probabilistic logic atmosphere causal discovery climate science information flow pathways probabilistic graphical model scientific computing

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A tree conditional random field model for panel detection in comic images

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PATTERN RECOGNITION 2015年第7期48卷 2129-2140页

作者： Li, Luyuan Wang, Yongtao Suen, Ching Y. Tang, Zhi Liu, Dong Peking Univ Beijing 100871 Peoples R China Concordia Univ Montreal PQ H3G 1M8 Canada

The goal of panel detection is to decompose the comic image into several panels (or frames), which is the fundamental step to produce digital comic books that are suitable for reading on mobile devices. The existing methods are limited in presenting the extracted panels as squares or rectangles and solely use one type of visual patterns, which are not generic in terms of handling comic images with multiple styles or complex layouts. To overcome the shortcomings of the existing approaches, we propose a novel method to detect panels within comic images. The method incorporates three types of visual patterns extracted from the comic image at different levels and a tree conditional random field framework is used to label each visual pattern by modeling its contextual dependencies. The final panel detection results are obtained by the visual pattern labels and a post-processing stage. Notably, the detected panels are presented as convex polygons in order to keep their content integrity. Experimental results demonstrate that the proposed method achieves better performance than the existing ones. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Comic image document Panel detection probabilistic graphical model Conditional random field Context modeling

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Representing and processing lineages over uncertain data based on the Bayesian network

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APPLIED SOFT COMPUTING 2015年 37卷 345-362页

作者： Yue, Kun Wu, Hao Liu, Weiyi Zhu, Yunlei Yunnan Univ Sch Informat Sci & Engn Dept Comp Sci & Engn Kunming 650504 Peoples R China

Processing lineages (also called provenances) over uncertain data consists in tracing the origin of uncertainty based on the process of data production and evolution. In this paper, we focus on the representation and processing of lineages over uncertain data, where we adopt Bayesian network (BN), one of the popular and important probabilistic graphical models (PGMs), as the framework of uncertainty representation and inferences. Starting from the lineage expressed as Boolean formulae for SPJ (Selection-Projection-Join) queries over uncertain data, we propose a method to transform the lineage expression into directed acyclic graphs (DAGs) equivalently. Specifically, we discuss the corresponding probabilistic semantics and properties to guarantee that the graphical model can support effective probabilistic inferences in lineage processing theoretically. Then, we propose the function-based method to compute the conditional probability table (CPT) for each node in the DAG. The BN for representing lineage expressions over uncertain data, called lineage BN and abbreviated as LBN, can be constructed while generally suitable for both safe and unsafe query plans. Therefore, we give the variable-elimination-based algorithm for LBN's exact inferences to obtain the probabilities of query results, called LBN-based query processing. Then, we focus on obtaining the probabilities of inputs or intermediate tuples conditioned on query results, called LBN-based inference query processing, and give the Gibbs-sampling-based algorithm for LBN's approximate inferences. Experimental results show the efficiency and effectiveness of our methods. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Uncertain data Lineage Inference query probabilistic graphical model Bayesian network Approximate inference

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Mathematical modeling of coastal marine environments using observational data for coastal management

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OCEAN & COASTAL MANAGEMENT 2015年 116卷 396-403页

作者： Kim, Jinah Park, Jinah KIOST Coastal Disaster Res Ctr Ansan South Korea Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea

Coastal development affects human life and economic activity. Given a necessity to develop coastal areas, there is a need for a method by which to understand and quantitatively assess and predict changes to coastal marine environments. In this paper, we propose a mathematical modeling for coastal marine environments using observational data. In particular, we establish probabilistic graphical model based on the data-driven statistical model for Saemangeum coast of Korea, where land reclamation work has been taking place. The derived model consists of latent and observation variables and their causal relationships. Ocean currents occurred by water exchange appear to be the key factor influencing the coastal marine environments in the artificial lake of Saemangeum coast. Hence, coastal water quality in the coastal management is the major concern by stakeholders. Using the proposed model we were able to compute the followings to demonstrate the usage of our proposed model: First, if the lake were to be entirely cut off from sea water exchange (which takes place through sluice gates in the sea dyke to the open sea), coastal water quality may deteriorated to approximately 37.5% of its current quality. Secondly, in order to maintain a minimum acceptable coastal water quality in the artificial lake, permitting its use in industrial supply and agriculture, currents of about 0.6 m/s are required for sea water exchange. This approach will assist in coastal management by supporting decision-making, policy planning, and the establishment of strategies for sustainable coastal development and conservation of coastal marine environments. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Coastal marine environments probabilistic graphical model probabilistic inference Coastal water quality Coastal management Sustainable coastal development

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Statistical Relational Artificial Intelligence: From Distributions through Actions to Optimization

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KUNSTLICHE INTELLIGENZ 2015年第4期29卷 363-368页

作者： Kersting, Kristian Natarajan, Sriraam TU Dortmund Univ Dortmund Germany Indiana Univ Bloomington IN USA

Statistical Relational AI-the science and engineering of making intelligent machines acting in noisy worlds composed of objects and relations among the objects-is currently motivating a lot of new AI research and has tremendous theoretical and practical implications. Theoretically, combining logic and probability in a unified representation and building general-purpose reasoning tools for it has been the dream of AI, dating back to the late 1980s. Practically, successful statistical relational AI tools enable new applications in several large, complex realworld domains including those involving big data, natural text, social networks, the web, medicine and robotics, among others. Such domains are often characterized by rich relational structure and large amounts of uncertainty. Logic helps to faithfully model the former while probability helps to effectively manage the latter. Our intention here is to give a brief (and necessarily incomplete) overview and invitation to the emerging field of Statistical Relational AI from the perspective of acting optimally and learning to act.

关键词： Inductive Logic Programming Logical Query Markov Decision Process probabilistic graphical model Situation Calculus

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