61692411
Visual learning by integrating descriptive and generative methods
This paper presents a mathematical framework for visual learning that integrates two popular statistical learning paradigms in the literature: (I). Descriptive learning, such as Markov random fields and minimax entropy learning, and (II). Generative learning, such as PCA, ICA, TCA, image coding and HMM. We apply this integrated learning framework to texton modeling, and we assume that an observed texture image is generated by multiple layers of hidden stochastic "texton processes" with each texton being a window function, like a mini-template or a wavelet, under affine transformations. The spatial arrangements of the textons are characterized by minimax entropy models. The texton processes generate images by occlusion or linear addition. Thus given a raw input image, the learning framework achieves four goals: (i). Computing the appearance of the textons. (ii) Inferring the hidden stochastic texton processes. (iii). Learning Gibbs models for each texton process and (iv). Verifying the learnt textons and Gibbs models through random sampling and texture synthesis. The integrated framework subsumes the minimax entropy learning paradigm and creates a richer class of probability models for visual patterns, which are suited for middle level vision representations. Furthermore we show that the integration of description and generative methods yields a natural and general framework of visual learning. We demonstrate the proposed framework and algorithms on many real images.
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.
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.
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.
15267641
Estimating Joint Probabilities from Marginal Ones
Estimating joint probabilities plays an important role in many data mining and machine learning tasks. In this paper we introduce two methods, minAB and prodAB, to estimate joint probabilities. Both methods are based on a light-weight structure, partition support. The core idea is to maintain the partition support of itemsets over logically disjoint partitions and then use it to estimate joint probabilities of item-sets of higher cardinalitiess. We present extensive mathematical analyses on both methods and compare their performances on synthetic datasets. We also demonstrate a case study of using the estimation methods in Apriori algorithm for fast association mining. Moreover, we explore the usefulness of the estimation methods in other mining/learning tasks. Experimental results show the effectiveness of the estimation methods.
42138946
An Evaluation of Colour Models' Performance Using Image Compression Algorithms
41478916
New Uniform Colour Spaces
36453049
Parallel Models and Job Characterization for System Scheduling
In this work, we study job characterization in multi-programmed multiprocessor system by taking into consideration the parallel job models. We first introduce an example to illustrate the issues involved. Then we focus on two popular system scheduling policies: round-robin for single processor systems, and equi-partition for multiprocessor systems. We analytically study effect of job parallelization on the overall performance of the system, and also present simulation results. Through these studies, we discuss scheduling policies of parallel jobs in relation to the ratio of number of jobs and the number of processors in the multiprocessor system, and propose new paradigm for parallel algorithm designs.
37613840
Improving medical/biological data classification performance by wavelet preprocessing
Many real-world datasets contain noise which could degrade the performances of learning algorithms. Motivated from the success of wavelet denoising techniques in image data, we explore a general solution to alleviate the effect of noisy data by wavelet preprocessing for medical/biological data classification. Our experiments are divided into two categories: one is of different classification algorithms on a specific database, and the other is of a specific classification algorithm (decision tree) on different databases. The experiment results show that the wavelet denoising of noisy data is able to improve the accuracies of those classification methods, if the localities of the attributes are strong enough.