Flash matting
In this paper, we propose a novel approach to extract mattes using a pair of flash/no-flash images. Our approach, which we call flash matting, was inspired by the simple observation that the most noticeable difference between the flash and no-flash images is the foreground object if the background scene is sufficiently distant. We apply a new matting algorithm called joint Bayesian flash matting to robustly recover the matte from flash/no-flash images, even for scenes in which the foreground and the background are similar or the background is complex. Experimental results involving a variety of complex indoors and outdoors scenes show that it is easy to extract high-quality mattes using an off-the-shelf, flash-equipped camera. We also describe extensions to flash matting for handling more general scenes.

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: Poisson matting
abstract: In this paper, we formulate the problem of natural image matting as one of solving Poisson equations with the matte gradient field. Our approach, which we call Poisson matting, has the following advantages. First, the matte is directly reconstructed from a continuous matte gradient field by solving Poisson equations using boundary information from a user-supplied trimap. Second, by interactively manipulating the matte gradient field using a number of filtering tools, the user can further improve Poisson matting results locally until he or she is satisfied. The modified local result is seamlessly integrated into the final result. Experiments on many complex natural images demonstrate that Poisson matting can generate good matting results that are not possible using existing matting techniques.

title: Picture Collage
abstract: 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: 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: Video object cut and paste
abstract: In this paper, we present a system for cutting a moving object out from a video clip. The cutout object sequence can be pasted onto another video or a background image. To achieve this, we first apply a new 3D graph cut based segmentation approach on the spatial-temporal video volume. Our algorithm partitions watershed presegmentation regions into foreground and background while preserving temporal coherence. Then, the initial segmentation result is refined locally. Given two frames in the video sequence, we specify two respective windows of interest which are then tracked using a bi-directional feature tracking algorithm. For each frame in between these two given frames, the segmentation in each tracked window is refined using a 2D graph cut that utilizes a local color model. Moreover, we provide brush tools for the user to control the object boundary precisely wherever needed. Based on the accurate binary segmentation result, we apply coherent matting to extract the alpha mattes and foreground colors of the object.

title: Latent Friend Mining from Blog Data
abstract: The rapid growth of blog (also known as "weblog") data provides a rich resource for social community mining. In this paper, we put forward a novel research problem of mining the latent friends of bloggers based on the contents of their blog entries. Latent friends are defined in this paper as people who share the similar topic distribution in their blogs. These people may not actually know each other, but they have the interest and potential to find each other out. Three approaches are designed for latent friend detection. The first one, called cosine similarity-based method, determines the similarity between bloggers by calculating the cosine similarity between the contents of the blogs. The second approach, known as topic-based method, is based on the discovery of latent topics using a latent topic model and then calculating the similarity at the topic level. The third one is two-level similarity-based, which is conducted in two stages. In the first stage, an existing topic hierarchy is exploited to build a topic distribution for a blogger. Then, in the second stage, a detailed similarity comparison is conducted for bloggers that are close in interest to each other which are discovered in the first stage. Our experimental results show that both the topic-based and two-level similarity-based methods work well, and the last approach performs much better than the first two. In this paper, we give a detailed analysis of the advantages and disadvantages of different approaches.

title: Text classification improved through multigram models
abstract: Classification algorithms and document representation approaches are two key elements for a successful document classification system. In the past, much work has been conducted to find better ways to represent documents. However, most of the attempts rely on certain extra resources such as WordNet, or they face the problem of extremely high dimension. In this paper, we propose a new document representation approach based on n-multigram language models. This approach can automatically discover the hidden semantic sequences in the documents under each category. Based on n-multigram language models and n-gram language models, we put forward two text classification algorithms. The experiments on RCV1 show that our proposed algorithm based on n-multigram models alone can achieve the similar or even better classification performance compared with the classifier based on n-gram models but the model size of our algorithm is much smaller than that of the latter. Another proposed algorithm based on the combination of n-multigram models and n-gram models improves the micro-F1 and macro-F1 values from 89.5% to 92.6% and 87.2% to 91.1% respectively. All these observations support the validity of our proposed document representation approach.

title: Efficient Tree-Based Revocation in Groups of Low-State Devices
abstract: We study the problem of broadcasting confidential information to a collection of n devices while providing the ability to revoke an arbitrary subset of those devices (and tolerating collusion among the revoked devices). In this paper, we restrict our attention to low-memory devices, that is, devices that can store at most O(log n) keys. We consider solutions for both zero-state and low-state cases, where such devices are organized in a tree structure T. We allow the group controller to encrypt broadcasts to any subtree of T, even if the tree is based on an multi-way organizational chart or a severely unbalanced multicast tree.

title: Formal semantics and verification for feature modeling
abstract: Research on features has received much attention in the domain engineering community. Feature modeling plays an important role in the design and implementation of complex software systems. However, the presentation and analysis of feature models are still largely informal. There is also an increasing need for methods and tools that can support automated feature model analysis. This paper presents a formal engineering approach to the specification and verification of feature models. A formal semantics for the feature modeling language is defined using first-order logic. It provides a precise and rigorous formal interpretation for the graphical notation. In addition, further validation of the semantics using the Z/EVES theorem prover is presented. Finally, we demonstrate that the consistency of a feature model and its configurations can be automatically verified by encoding the semantics into the Alloy Analyzer. A case study of the Key Word in Context (KWIC) index systems feature model is presented to illustrate the verification process.

title: Efficient Immunization Algorithm for Peer-to-Peer Networks
abstract: In this paper, we present a detail study about the immunization of viruses in Peer-to-Peer networks exhibiting power-law degree distributions By comparing two different immunization strategies (randomized and degree-based), we conclude that it is efficient to immunize the highly connected nodes in order to eradicate viruses from the network Furthermore, we propose an efficient updating algorithm for global virus database according to the degree-based immunization strategy.