Picture Collage
In this paper, we address a novel problem of automatically creating a picture collage from a group of images. Picture collage is a kind of visual image summary-to arrange all input images on a given canvas, allowing overlay, to maximize visible visual information. We formulate the picture collage creation problem in a conditional random field model, which integrates image salience, canvas constraint, natural preference, and user interaction. Each image is represented by a group of weighted rectangles, which indicate the salient regions. Then picture collage is resolved by minimizing the energy, guided by the constraints. A two-step optimization method is proposed. First, a quick initialization algorithm based on the proposed 1D collage method is presented. Second, a very efficient Markov chain Monte Carlo method is designed for the refined optimization. We also integrate user interaction in the formulation and optimization to obtain an interactive collage reflecting personalized preference. Visual and quantitative experimental evaluations indicate the efficiency of the proposed collage creation technique.

title: Background Cut
abstract: In this paper, we introduce background cut, a high quality and real-time foreground layer extraction algorithm. From a single video sequence with a moving foreground object and stationary background, our algorithm combines background subtraction, color and contrast cues to extract a foreground layer accurately and efficiently. The key idea in background cut is background contrast attenuation, which adaptively attenuates the contrasts in the background while preserving the contrasts across foreground/background boundaries. Our algorithm builds upon a key observation that the contrast (or more precisely, color image gradient) in the background is dissimilar to the contrast across foreground/background boundaries in most cases. Using background cut, the layer extraction errors caused by background clutter can be substantially reduced. Moreover, we present an adaptive mixture model of global and per-pixel background colors to improve the robustness of our system under various background changes. Experimental results of high quality composite video demonstrate the effectiveness of our background cut algorithm.

title: Stereo Matching Using Belief Propagation
abstract: In this paper, we formulate the stereo matching problem as a Markov network consisting of three coupled Markov random fields (MRF's). These three MRF's model a smooth field for depth/disparity, a line process for depth discontinuity and a binary process for occlusion, respectively. After eliminating the line process and the binary process by introducing two robust functions, we obtain the maximum a posteriori (MAP) estimation in the Markov network by applying a Bayesian belief propagation (BP) algorithm. Furthermore, we extend our basic stereo model to incorporate other visual cues (e.g., image segmentation) that are not modeled in the three MRF's, and again obtain the MAP solution. Experimental results demonstrate that our method outperforms the state-of-art stereo algorithms for most test cases.

title: Verification of Computation Orchestration Via Timed Automata
abstract: Recently, a promising programming model called Orc has been proposed to support a structured way of orchestrating distributed web services. Orc is intuitive because it offers concise constructors to manage concurrent communication, time-outs, priorities, failure of sites or communication and so forth. The semantics of Orc is also precisely defined. However, there is no verification tool available to verify critical properties against Orc models. Instead of building one from scratch, we believe the existing mature model-checkers can be reused. In this work, we first define a Timed Automata semantics for the Orc language, which we prove is semantically equivalent to the original operational semantics of Orc. Consequently, Timed Automata models are systematically constructed from Orc models. The practical implication of the construction is that tool supports for Timed Automata, e.g., UPPAAL, can be used to model check Orc models. An experimental tool is implemented to automate our approach.

title: CubeSVD: a novel approach to personalized Web search
abstract: As the competition of Web search market increases, there is a high demand for personalized Web search to conduct retrieval incorporating Web users' information needs. This paper focuses on utilizing clickthrough data to improve Web search. Since millions of searches are conducted everyday, a search engine accumulates a large volume of clickthrough data, which records who submits queries and which pages he/she clicks on. The clickthrough data is highly sparse and contains different types of objects (user, query and Web page), and the relationships among these objects are also very complicated. By performing analysis on these data, we attempt to discover Web users' interests and the patterns that users locate information. In this paper, a novel approach CubeSVD is proposed to improve Web search. The clickthrough data is represented by a 3-order tensor, on which we perform 3-mode analysis using the higher-order singular value decomposition technique to automatically capture the latent factors that govern the relations among these multi-type objects: users, queries and Web pages. A tensor reconstructed based on the CubeSVD analysis reflects both the observed interactions among these objects and the implicit associations among them. Therefore, Web search activities can be carried out based on CubeSVD analysis. Experimental evaluations using a real-world data set collected from an MSN search engine show that CubeSVD achieves encouraging search results in comparison with some standard methods.

title: A Reasoning Method for Timed CSP Based on Constraint Solving
abstract: Timed CSP extends CSP by introducing a capability to quantify temporal aspects of sequencing and synchronization. It is a powerful language to model real time reactive systems. However, there is no verification tool support for proving critical properties over systems modelled using Timed CSP. In this work, we construct a reasoning method using Constraint Logic Programming (CLP) as an underlying reasoning mechanism for Timed CSP. We start with encoding the semantics of Timed CSP in CLP, which allows a systematic translation of Timed CSP to CLP. Powerful constraint solver like CLP($\mathcal{R}$) is then used to prove traditional safety properties and beyond, e.g., reachability, deadlock-freeness, timewise refinement relationship, lower or upper bound of a time interval, etc. Counter-examples are generated when properties are not satisfied. Moreover, our method also handles useful extensions to Timed CSP. Finally, we demonstrate the effectiveness of our approach through case study of standard real time systems.

title: Distributed Pattern Discovery in Multiple Streams
abstract: Given m groups of streams which consist of n1,...,nm co-evolving streams in each group, we want to: (i) incrementally find local patterns within a single group, (ii) efficiently obtain global patterns across groups, and more importantly, (iii) efficiently do that in real time while limiting shared information across groups. In this paper, we present a distributed, hierarchical algorithm addressing these problems. Our experimental case study confirms that the proposed method can perform hierarchical correlation detection efficiently and effectively.

title: Reasoning Support for SWRL-FOL Using Alloy