6046438
A high resolution grammatical model for face representation and sketching
In this paper we present a generative, high resolution face representation which extends the well-known active appearance model (AAM) with two additional layers. (i) One layer refines the global AAM (PCA) model with a dictionary of learned face components to account for the shape and intensity variabilities of eyes, eyebrows, nose and mouth, (ii) the other layer divides the face skin into 9 zones with a learned dictionary of sketch primitives to represent skin marks and wrinkles. This model is no longer of fixed dimensions and is flexible for it can select the diverse representations in the dictionaries of face components and skin features depending on the complexity of the face. The selection is modulated by the grammatical rules through hidden "switch " variables. Our comparison experiments demonstrate that this model can achieve nearly lossless coding of face at high resolution (256 /spl times/ 256 pixels) with low bits. We also show that the generative model can easily generate cartoon sketches by changing the rendering dictionary. Our face model is aimed at a number of applications including cartoon sketch in non-photorealistic rendering, super-resolution in image processing, and low bit face communication in wireless platforms.
12345366
Image segmentation by data driven Markov chain Monte Carlo
This paper presents a computational paradigm called Data Driven Markov Chain Monte Carlo (DDMCMC) for image segmentation in the Bayesian, statistical framework. The paper contributes to image segmentation in three aspects. Firstly, it designs effective and well balanced Markov Chain dynamics to explore the solution space and makes the split and merge process reversible at a middle level vision formulation. Thus it achieves globally optimal solution independent of initial segmentations. Secondly, instead of computing a single maximum a posteriori solution, it proposes a mathematical principle for computing multiple distinct solutions to incorporates intrinsic ambiguities in image segmentation. A k-adventurers algorithm is proposed for extracting distinct multiple solutions from the Markov chain sequence. Thirdly, it utilizes data-driven (bottom-up) techniques, such as clustering and edge detection, to compute importance proposal probabilities, which effectively drive the Markov chain dynamics and achieve tremendous speedup in comparison to traditional jump-diffusion method. Thus DDM-CMC paradigm provides a unifying framework where the role of existing segmentation algorithms, such as; edge detection, clustering, region growing, split-merge, SNAKEs, region competition, are revealed as either realizing Markov chain dynamics or computing importance proposal probabilities. We report some results on color and grey level image segmentation in this paper and refer to a detailed report and a web site for extensive discussion.
206590834
Composite Templates for Cloth Modeling and Sketching
Cloth modeling and recognition is an important and challenging problem in both vision and graphics tasks, such as dressed human recognition and tracking, human sketch and portrait. In this paper, we present a context sensitive grammar in an And-Or graph representation which will produce a large set of composite graphical templates to account for the wide variabilities of cloth configurations, such as T-shirts, jackets, etc. In a supervised learning phase, we ask an artist to draw sketches on a set of dressed people, and we decompose the sketches into categories of cloth and body components: collars, shoulders, cuff, hands, pants, shoes etc. Each component has a number of distinct subtemplates (sub-graphs). These sub-templates serve as leafnodes in a big And-Or graph where an And-node represents a decomposition of the graph into sub-configurations with Markov relations for context and constraints (soft or hard), and an Or-node is a switch for choosing one out of a set of alternative And-nodes (sub-configurations) - similar to a node in stochastic context free grammar (SCFG). This representation integrates the SCFG for structural variability and the Markov (graphical) model for context. An algorithm which integrates the bottom-up proposals and the topdown information is proposed to infer the composite cloth template from the image.
122817250
Modeling complex motion by tracking and editing hidden Markov graphs
We propose a generative model for representing complex motion, such as wavy river, dancing fire and dangling cloth. Our generative method consists of four components: (1) A photometric model using primal sketch[8] which transfers an image into an attribute graph representation. Each vertex of the graph is a scaled and oriented image patch selected from a dictionary. The graph connects and aligns these patches. (2) A geometric model which characterizes the deformation of the attribute graph. (3) A dynamic model, which specifies the motion dynamics of these vertices (patches) and their interactions in the form of coupled Markov chains. (4) A topological model, which interprets the graph topological changes over time. We learn this generative model by a stochastic gradient algorithm implemented by Markov Chain Monte Carlo (MCMC) sampling. This method is shown to be effective in handling the topological changes of graphs. The correctness of the learned model is verified by the low-dimension reconstruction of the original image as well as by the realistic motion sequences it synthesized.
398989
Parsing Images into Regions, Curves, and Curve Groups
In this paper, we present an algorithm for parsing natural images into middle level vision representations—regions, curves, and curve groups (parallel curves and trees). This algorithm is targeted for an integrated solution to image segmentation and curve grouping through Bayesian inference. The paper makes the following contributions. (1) It adopts a layered (or 2.1D-sketch) representation integrating both region and curve models which compete to explain an input image. The curve layer occludes the region layer and curves observe a partial order occlusion relation. (2) A Markov chain search scheme Metropolized Gibbs Samplers (MGS) is studied. It consists of several pairs of reversible jumps to traverse the complex solution space. An MGS proposes the next state within the jump scope of the current state according to a conditional probability like a Gibbs sampler and then accepts the proposal with a Metropolis-Hastings step. This paper discusses systematic design strategies of devising reversible jumps for a complex inference task. (3) The proposal probability ratios in jumps are factorized into ratios of discriminative probabilities. The latter are computed in a bottom-up process, and they drive the Markov chain dynamics in a data-driven Markov chain Monte Carlo framework. We demonstrate the performance of the algorithm in experiments with a number of natural images.
122560265
Multigrid and multi-level Swendsen-Wang cuts for hierarchic graph partition
Many vision tasks can be formulated as partitioning an adjacency graph through optimizing a Bayesian posterior probability p defined on the partition-space. In this paper two approaches are proposed to generalize the Swendsen-Wang cut algorithm [A. Barbu and S.C. Zhu 2003] for sampling p. The first method is called multigrid SW-cut which runs SW-cut within a sequence of local "attentional" windows and thus simulates conditional probabilities of p in the partition space. The second method is called multi-level SW-cut which projects the adjacency graph into a hierarchical representation with each vertex in the high level graph corresponding to a sub-graph at the low level, and runs SW-cut at each level. Thus it simulates conditional probabilities of p at the higher level. Both methods are shown to observe the detailed balance equation and thus provide flexibilities in sampling the posterior probability p. We demonstrate the algorithms in image and motion segmentation with three levels (see Fig.1), and compare the speed improvement of the proposed methods.
1527832
Document clustering with cluster refinement and model selection capabilities
In this paper, we propose a document clustering method that strives to achieve: (1) a high accuracy of document clustering, and (2) the capability of estimating the number of clusters in the document corpus (i.e. the model selection capability). To accurately cluster the given document corpus, we employ a richer feature set to represent each document, and use the Gaussian Mixture Model (GMM) together with the Expectation-Maximization (EM) algorithm to conduct an initial document clustering. From this initial result, we identify a set of discriminative featuresfor each cluster, and refine the initially obtained document clusters by voting on the cluster label of each document using this discriminative feature set. This self-refinement process of discriminative feature identification and cluster label voting is iteratively applied until the convergence of document clusters. On the other hand, the model selection capability is achieved by introducing randomness in the cluster initialization stage, and then discovering a value C for the number of clusters N by which running the document clustering process for a fixed number of times yields sufficiently similar results. Performance evaluations exhibit clear superiority of the proposed method with its improved document clustering and model selection accuracies. The evaluations also demonstrate how each feature as well as the cluster refinement process contribute to the document clustering accuracy.
15097946
Algorithms for clustering high dimensional and distributed data
Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. The clustering problem has been widely studied in machine learning, databases, and statistics. This paper studies the problem of clustering high dimensional data. The paper proposes an algorithm called the CoFD algorithm, which is a non-distance based clustering algorithm for high dimensional spaces. Based on the Maximum Likelihood Principle, CoFD attempts to optimize its parameter settings to maximize the likelihood between data points and the model generated by the parameters. The distributed versions of the problem, called the D-CoFD algorithms, are also proposed. Experimental results on both synthetic and real data sets show the efficiency and effectiveness of CoFD and D-CoFD algorithms.
13451366
Comparing Different Colour Discrimination Data Sets
15435799
Structural and temporal analysis of the blogosphere through community factorization
The blogosphere has unique structural and temporal properties since blogs are typically used as communication media among human individuals. In this paper, we propose a novel technique that captures the structure and temporal dynamics of blog communities. In our framework, a community is a set of blogs that communicate with each other triggered by some events (such as a news article). The community is represented by its structure and temporal dynamics: a community graph indicates how often one blog communicates with another, and a community intensity indicates the activity level of the community that varies over time. Our method, community factorization, extracts such communities from the blogosphere, where the communication among blogs is observed as a set of subgraphs (i.e., threads of discussion). This community extraction is formulated as a factorization problem in the framework of constrained optimization, in which the objective is to best explain the observed interactions in the blogosphere over time. We further provide a scalable algorithm for computing solutions to the constrained optimization problems. Extensive experimental studies on both synthetic and real blog data demonstrate that our technique is able to discover meaningful communities that are not detectable by traditional methods.
10016327
Identifying early buyers from purchase data
Market research has shown that consumers exhibit a variety of different purchasing behaviors; specifically, some tend to purchase products earlier than other consumers. Identifying such early buyers can help personalize marketing strategies, potentially improving their effectiveness. In this paper, we present a non-parametric approach to the problem of identifying early buyers from purchase data. Our formulation takes as inputs the detailed purchase information of each consumer, with which we construct a weighted directed graph whose nodes correspond to consumers and whose edges correspond to purchases consumers have in common; the edge weights indicate how frequently consumers purchase products earlier than other consumers.Identifying early buyers corresponds to the problem of finding a subset of nodes in the graph with maximum difference between the weights of the outgoing and incoming edges. This problem is a variation of the maximum cut problem in a directed graph. We provide an approximation algorithm based on semidefinite programming (SDP) relaxations pioneered by Goemans and Williamson, and analyze its performance. We apply the algorithm to real purchase data from Amazon.com, providing new insights into consumer behaviors.