Results 1  10
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48
Random walks for image segmentation
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2006
"... Abstract—A novel method is proposed for performing multilabel, interactive image segmentation. Given a small number of pixels with userdefined (or predefined) labels, one can analytically and quickly determine the probability that a random walker starting at each unlabeled pixel will first reach on ..."
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Cited by 218 (18 self)
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Abstract—A novel method is proposed for performing multilabel, interactive image segmentation. Given a small number of pixels with userdefined (or predefined) labels, one can analytically and quickly determine the probability that a random walker starting at each unlabeled pixel will first reach one of the prelabeled pixels. By assigning each pixel to the label for which the greatest probability is calculated, a highquality image segmentation may be obtained. Theoretical properties of this algorithm are developed along with the corresponding connections to discrete potential theory and electrical circuits. This algorithm is formulated in discrete space (i.e., on a graph) using combinatorial analogues of standard operators and principles from continuous potential theory, allowing it to be applied in arbitrary dimension on arbitrary graphs. Index Terms—Image segmentation, interactive segmentation, graph theory, random walks, combinatorial Dirichlet problem, harmonic functions, Laplace equation, graph cuts, boundary completion. Ç 1
Segmentation of Multiple Salient Closed Contours from Real Images
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2003
"... Using a saliency measure based on the global property of contour closure, we have developed a segmentation method which identifies smooth closed contours bounding objects of unknown shape in real images. The saliency measure incorporates the Gestalt principles of proximity and good continuity that ..."
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Cited by 58 (1 self)
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Using a saliency measure based on the global property of contour closure, we have developed a segmentation method which identifies smooth closed contours bounding objects of unknown shape in real images. The saliency measure incorporates the Gestalt principles of proximity and good continuity that previous methods have also exploited. Unlike previous methods, we incorporate contour closure by finding the eigenvector with the largest positive real eigenvalue of a transition matrix for aMarkov process whereedges from the image serve as states. Element i; j of the transition matrix is the conditional probability that a contour which contains edge j will also contain edge i. In this paper, we show how the saliency measure, defined for individual edges, can be used to derive a saliency relation, defined for pairs of edges, and further show that stronglyconnected components of the graph representing the saliency relation correspond to smooth closed contours in the image. Finally, we report for the first time, results on large real images for which segmentation takes an average of about 10 seconds per object on a generalpurpose workstation.
ManMade Structure Detection in Natural Images using a Causal Multiscale Random Field
 In Proc. IEEE Int. Conf. on Comp. Vision and Pattern Recog
"... This paper presents a generative model based approach to manmade structure detection in 2D natural images. The proposed approach uses a causal multiscale random field suggested in [3] as a prior model on the class labels on the image sites. However, instead of assuming the conditional independence ..."
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Cited by 53 (3 self)
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This paper presents a generative model based approach to manmade structure detection in 2D natural images. The proposed approach uses a causal multiscale random field suggested in [3] as a prior model on the class labels on the image sites. However, instead of assuming the conditional independence of the observed data, we propose to capture the local dependencies in the data using a multiscale feature vector. The distribution of the multiscale feature vectors is modeled as mixture of Gaussians. A set of robust multiscale features is presented that captures the general statistical properties of manmade structures at multiple scales without relying on explicit edge detection. The proposed approach was validated on realworld images from the Corel data set, and a performance comparison with other techniques is presented.
Sastry,”Varieties of Learning Automata: An Overview
 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS
, 2002
"... Abstract—Automata models of learning systems introduced in the 1960s were popularized as learning automata (LA) in a survey paper in 1974 [1]. Since then, there have been many fundamental advances in the theory as well as applications of these learning models. In the past few years, the structure of ..."
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Cited by 51 (0 self)
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Abstract—Automata models of learning systems introduced in the 1960s were popularized as learning automata (LA) in a survey paper in 1974 [1]. Since then, there have been many fundamental advances in the theory as well as applications of these learning models. In the past few years, the structure of LA has been modified in several directions to suit different applications. Concepts such as parameterized learning automata (PLA), generalized learning automata (GLA), and continuous actionset learning automata (CALA) have been proposed, analyzed, and applied to solve many significant learning problems. Furthermore, groups of LA forming teams and feedforward networks have been shown to converge to desired solutions under appropriate learning algorithms. Modules of LA have been used for parallel operation with consequent increase in speed of convergence. All of these concepts and results are relatively new and are scattered in technical literature. An attempt has been made in this paper to bring together the main ideas involved in a unified framework and provide pointers to relevant references. Index Terms—Continuous actionset learning automata (CALA), generalized learning automata (GLA), modules of learning automata, parameterized learning automata (PLA), teams and networks of learning automata. I.
CLUE: Clusterbased Retrieval of Images by Unsupervised Learning
 IEEE Transactions on Image Processing
, 2003
"... In a typical contentbased image retrieval (CBIR) system, query results are a set of images sorted by feature similarities with respect to the query. However, images with high feature similarities to the query may be very di#erent from the query in terms of semantics. This discrepancy between lowle ..."
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Cited by 45 (2 self)
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In a typical contentbased image retrieval (CBIR) system, query results are a set of images sorted by feature similarities with respect to the query. However, images with high feature similarities to the query may be very di#erent from the query in terms of semantics. This discrepancy between lowlevel features and highlevel concepts is known as the semantic gap. This paper introduces a novel image retrieval scheme, CLUsterbased rEtrieval of images by unsupervised learning (CLUE), which attempts to tackle the semantic gap problem based on a hypothesis that images of the same semantics are similar in a way, images of di#erent semantics are di#erent in their own ways. CLUE attempts to capture semantic concepts by learning the way that images of the same semantics are similar and retrieving image clusters instead of a set of ordered images. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. Therefore, the clusters give the algorithm as well as the users semantic relevant clues as to where to navigate. CLUE is a general approach that can be combined with any realvalued symmetric similarity measure (metric or nonmetric). Thus it may be embedded in many current CBIR systems. An experimental image retrieval system using CLUE has been implemented. The performance of the system is evaluated on a database of about 60, 000 images from COREL. Empirical results demonstrate improved performance compared with a typical CBIR system using the same image similarity measure. In addition, preliminary results on images returned by Google's Image Search reveal the potential of applying CLUE to real world image data and integrating CLUE as a part of the interface for keywordbased image retrieval systems.
Isoperimetric graph partitioning for image segmentation
 IEEE Trans. on Pat. Anal. and Mach. Int
, 2006
"... Abstract—Spectral graph partitioning provides a powerful approach to image segmentation. We introduce an alternate idea that finds partitions with a small isoperimetric constant, requiring solution to a linear system rather than an eigenvector problem. This approach produces the high quality segment ..."
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Cited by 40 (11 self)
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Abstract—Spectral graph partitioning provides a powerful approach to image segmentation. We introduce an alternate idea that finds partitions with a small isoperimetric constant, requiring solution to a linear system rather than an eigenvector problem. This approach produces the high quality segmentations of spectral methods, but with improved speed and stability. Index Terms—Graphtheoretic methods, graphs and networks, graph algorithms, image representation, special architectures, algorithms, computer vision, applications. æ 1
Untangling Cycles for Contour Grouping
"... We introduce a novel topological formulation for contour grouping. Our grouping criterion, called untangling cycles, exploits the inherent topological 1D structure of salient contours to extract them from the otherwise 2D image clutter. To define a measure for topological classification robust to cl ..."
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Cited by 40 (10 self)
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We introduce a novel topological formulation for contour grouping. Our grouping criterion, called untangling cycles, exploits the inherent topological 1D structure of salient contours to extract them from the otherwise 2D image clutter. To define a measure for topological classification robust to clutter and broken edges, we use a graph formulation instead of the standard computational topology. The key insight is that a pronounced 1D contour should have a clear ordering of edgels, to which all graph edges adhere, and no long range entanglements persist. Finding the contour grouping by optimizing these topological criteria is challenging. We introduce a novel concept of circular embedding to encode this combinatorial task. Our solution leads to computing the dominant complex eigenvectors/eigenvalues of the random walk matrix of the contour grouping graph. We demonstrate major improvements over stateoftheart approaches on challenging real images. 1.
Salient Closed Boundary Extraction with Ratio Contour
 IEEE Trans. on Pattern Analysis and Machine Intelligence
, 2005
"... We present ratio contour, a novel graphbased method for extracting salient closed boundaries from noisy images. This method operates on a set of boundary fragments that are produced by edge detection. Boundary extraction identifies a subset of these fragments and connects them sequentially to for ..."
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Cited by 39 (9 self)
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We present ratio contour, a novel graphbased method for extracting salient closed boundaries from noisy images. This method operates on a set of boundary fragments that are produced by edge detection. Boundary extraction identifies a subset of these fragments and connects them sequentially to form a closed boundary with the largest saliency. We encode the Gestalt laws of proximity and continuity in a novel boundarysaliency measure based on the relative gap length and average curvature when connecting fragments to form a closed boundary. This new measure attempts to remove a possible bias toward short boundaries. We present a polynomialtime algorithm for finding the mostsalient closed boundary. We also present supplementary preprocessing steps that facilitate the application of ratio contour to real images. We compare ratio contour to two closely related methods for extracting closed boundaries: Elder and Zucker's method based on the shortestpath algorithm and Williams and Thornber's method based on spectral analysis and a stronglyconnectedcomponents algorithm. This comparison involves both theoretic analysis and experimental evaluation on both synthesized data and real images.
Improved rooftop detection in aerial images with machine learning
 Machine Learning
, 2003
"... Abstract. In this paper, we examine the use of machine learning to improve a rooftop detection process, one step in a vision system that recognizes buildings in overhead imagery. We review the problem of analyzing aerial images and describe an existing system that detects buildings in such images. W ..."
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Cited by 16 (2 self)
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Abstract. In this paper, we examine the use of machine learning to improve a rooftop detection process, one step in a vision system that recognizes buildings in overhead imagery. We review the problem of analyzing aerial images and describe an existing system that detects buildings in such images. We briefly detail four algorithms that we selected to improve rooftop detection. The data sets were highly skewed and the cost of mistakes differed between the classes, so we used ROC analysis to evaluate the methods under varying error costs. We report three experiments designed to illuminate facets of applying machine learning to the image analysis task. One investigated learning with all available images to determine the best performing method. Another focused on withinimage learning, in which we derived training and testing data from the same image. A final experiment addressed betweenimage learning, in which training and testing sets came from different images. Results suggest that useful generalization occurred when training and testing on data derived from images differing in location and in aspect. They demonstrate that under most conditions, naive Bayes exceeded the accuracy of other methods and a handcrafted classifier, the solution currently used in the building detection system.