Results 1 - 10 of 226

Mean shift: A robust approach toward feature space analysis

by Dorin Comaniciu, Peter Meer - In PAMI , 2002
"... A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data the convergence ..."
Abstract - Cited by 2395 (37 self) - Add to MetaCart
A general nonparametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure, the mean shift. We prove for discrete data the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and thus its utility in detecting the modes of the density. The equivalence of the mean shift procedure to the Nadaraya–Watson estimator from kernel regression and the robust M-estimators of location is also established. Algorithms for two low-level vision tasks, discontinuity preserving smoothing and image segmentation are described as applications. In these algorithms the only user set parameter is the resolution of the analysis, and either gray level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.
(Show Context)

Efficient graph-based image segmentation.

by Pedro F Felzenszwalb , Daniel P Huttenlocher - International Journal of Computer Vision, , 2004
"... Abstract. This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show ..."
Abstract - Cited by 940 (1 self) - Add to MetaCart
Abstract. This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties. We apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph, and illustrate the results with both real and synthetic images. The algorithm runs in time nearly linear in the number of graph edges and is also fast in practice. An important characteristic of the method is its ability to preserve detail in low-variability image regions while ignoring detail in high-variability regions.
(Show Context)

Stereo matching using belief propagation

by Jian Sun, Nan-ning Zheng, Heung-yeung Shum , 2003
"... In this paper, we formulate the stereo matching problem as a Markov network and solve it using Bayesian belief propagation. The stereo Markov network consists of three coupled Markov random fields that model the following: a smooth field for depth/disparity, a line process for depth discontinuity, ..."
Abstract - Cited by 350 (4 self) - Add to MetaCart
In this paper, we formulate the stereo matching problem as a Markov network and solve it using Bayesian belief propagation. The stereo Markov network consists of three coupled Markov random fields that model the following: a smooth field for depth/disparity, a line process for depth discontinuity, and a binary process for occlusion. After eliminating the line process and the binary process by introducing two robust functions, we apply the belief propagation algorithm to obtain the maximum a posteriori (MAP) estimation in the Markov network. Other low-level visual cues (e.g., image segmentation) can also be easily incorporated in our stereo model to obtain better stereo results. Experiments demonstrate that our methods are comparable to the state-of-the-art stereo algorithms for many test cases.
(Show Context)

Unsupervised Segmentation of Color-Texture Regions in Images and Video

by B. S. Manjunath, Yining Deng, Yining Deng , 2001
"... A new method for unsupervised segmentation of color-texture regions in images and video is presented. This method, which we refer to as JSEG, consists of two independent steps: color quantization and spatial segmentation. In the first step, colors in the image are quantized to several representative ..."
Abstract - Cited by 318 (3 self) - Add to MetaCart
A new method for unsupervised segmentation of color-texture regions in images and video is presented. This method, which we refer to as JSEG, consists of two independent steps: color quantization and spatial segmentation. In the first step, colors in the image are quantized to several representative classes that can be used to differentiate regions in the image. The image pixels are then replaced by their corresponding color class labels, thus forming a class-map of the image. The focus of this work is on spatial segmentation, where a criterion for "good" segmentation using the class-map is proposed. Applying the criterion to local windows in the class-map results in the "Jimage, " in which high and low values correspond to possible boundaries and interiors of colortexture regions. A region growing method is then used to segment the image based on the multiscale J-images. A similar approach is applied to video sequences. An additional region tracking scheme is embedded into the region growing process to achieve consistent segmentation and tracking results, even for scenes with non-rigid object motion. Experiments show the robustness of the JSEG algorithm on real images and video.

Using Multiple Segmentations to Discover Objects and their Extent in Image Collections

by Bryan C. Russell, Alexei A. Efros, Josef Sivic, William T. Freeman, Andrew Zisserman - CVPR
"... Given a large dataset of images, we seek to automatically determine the visually similar object and scene classes together with their image segmentation. To achieve this we combine two ideas: (i) that a set of segmented objects can be partitioned into visual object classes using topic discovery mode ..."
Abstract - Cited by 315 (26 self) - Add to MetaCart
Given a large dataset of images, we seek to automatically determine the visually similar object and scene classes together with their image segmentation. To achieve this we combine two ideas: (i) that a set of segmented objects can be partitioned into visual object classes using topic discovery models from statistical text analysis; and (ii) that visual object classes can be used to assess the accuracy of a segmentation. To tie these ideas together we compute multiple segmentations of each image and then: (i) learn the object classes; and (ii) choose the correct segmentations. We demonstrate that such an algorithm succeeds in automatically discovering many familiar objects in a variety of image datasets, including those from Caltech, MSRC and LabelMe. 1.
(Show Context)

Mean Shift Analysis and Applications

by Dorin Comaniciu, Peter Meer , 1999
"... A nonparametric estimator of density gradient, the mean shift, is employed in the joint, spatial-range (value) domain of gray level and color images for discontinuity preserving filtering and image segmentation. Properties of the mean shift are reviewed and its convergence on lattices is proven. The ..."
Abstract - Cited by 200 (9 self) - Add to MetaCart
A nonparametric estimator of density gradient, the mean shift, is employed in the joint, spatial-range (value) domain of gray level and color images for discontinuity preserving filtering and image segmentation. Properties of the mean shift are reviewed and its convergence on lattices is proven. The proposed filtering method associates with each pixel in the image the closest local mode in the density distribution of the joint domain. Segmentation into a piecewise constant structure requires only one more step, fusion of the regions associated with nearby modes. The proposed technique has two parameters controlling the resolution in the spatial and range domains. Since convergence is guaranteed, the technique does not require the intervention of the user to stop the filtering at the desired image quality. Several examples, for gray and color images, show the versatilityofthe method and compare favorably with results described in the literature for the same images.
(Show Context)

Color image segmentation: Advances and prospects

by H. D. Cheng, X. H. Jiang, Y. Sun, Jing Li Wang - Pattern Recognition , 2001
"... Image segmentation is very essential and critical to image processing and pattern recognition. This survey provides a summary of color image segmentation techniques available now. Basically, color segmentation approaches are based on monochrome segmentation approaches operating in di erent color spa ..."
Abstract - Cited by 199 (5 self) - Add to MetaCart
Image segmentation is very essential and critical to image processing and pattern recognition. This survey provides a summary of color image segmentation techniques available now. Basically, color segmentation approaches are based on monochrome segmentation approaches operating in di erent color spaces. Therefore, we rst discuss the major segmentation approaches for segmenting monochrome images: histogram thresholding, characteristic feature clustering, edge detection, region-based methods, fuzzy techniques, neural networks, etc. � then review some major color representation methods and their advantages/disadvantages� nally summarize the color image segmentation techniques using di erent color representations. The usage of color models for image segmentation is also discussed. Some novel approaches such as fuzzy method and physics based method are investigated as well.
(Show Context)

A survey of content-based image retrieval with high-level semantics

by Ying Liu , Dengsheng Zhang , Guojun Lu , Wei-ying Ma , 2007
"... In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the ‘semantic gap ’ between the visual features and the richness of human semantics. This paper attemp ..."
Abstract - Cited by 150 (5 self) - Add to MetaCart
In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the ‘semantic gap ’ between the visual features and the richness of human semantics. This paper attempts to provide a comprehensive survey of the recent technical achievements in high-level semantic-based image retrieval. Major recent publications are included in this survey covering different aspects of the research in this area, including low-level image feature extraction, similarity measurement, and deriving high-level semantic features. We identify five major categories of the state-of-the-art techniques in narrowing down the ‘semantic gap’: (1) using object ontology to define high-level concepts; (2) using machine learning methods to associate low-level features with query concepts; (3) using relevance feedback to learn users’ intention; (4) generating semantic template to support high-level image retrieval; (5) fusing the evidences from HTML text and the visual content of images for WWW image retrieval. In addition, some other related issues such as image test bed and retrieval performance evaluation are also discussed. Finally, based on existing technology and the demand from real-world applications, a few promising future research directions are suggested.

Statistical Region Merging

by Richard Nock, Frank Nielsen - IEEE Trans. on Pattern Analysis and Machine Intelligence , 2004
"... This paper explores a statistical basis for a process often described in computer vision: image segmentation by region merging following a particular order in the choice of regions. We exhibit a particular blend of algorithmics and statistics whose segmentation error is, as we show, limited from b ..."
Abstract - Cited by 129 (9 self) - Add to MetaCart
This paper explores a statistical basis for a process often described in computer vision: image segmentation by region merging following a particular order in the choice of regions. We exhibit a particular blend of algorithmics and statistics whose segmentation error is, as we show, limited from both the qualitative and quantitative standpoints. This approach can be efficiently approximated in linear time/space, leading to a fast segmentation algorithm tailored to processing images described using most common numerical pixel attribute spaces. The conceptual simplicity of the approach makes it simple to modify and cope with hard noise corruption, handle occlusion, authorize the control of the segmentation scale, and process unconventional data such as spherical images. Experiments on gray-level and color images, obtained with a short readily available C-code, display the quality of the segmentations obtained.
(Show Context)

Image Segmentation Using Local Variation

by Pedro F. Felzenszwalb, Daniel P. Huttenlocher - in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition , 1998
"... We present a new graph-theoretic approach to the problem of image segmentation. Our method uses local criteria and yet produces results that reflect global properties of the image. We develop a framework that provides specific definitions of what it means for an image to be under- or over-segmented. ..."
Abstract - Cited by 114 (3 self) - Add to MetaCart
We present a new graph-theoretic approach to the problem of image segmentation. Our method uses local criteria and yet produces results that reflect global properties of the image. We develop a framework that provides specific definitions of what it means for an image to be under- or over-segmented. We then present an efficient algorithm for computing a segmentation that is neither under- nor over-segmented according to these definitions. Our segmentation criterion is based on intensity differences between neighboring pixels. An important characteristic of the approach is that it is able to preserve detail in low-variability regions while ignoring detail in high-variability regions, which we illustrate with several examples on both real and sythetic images.
(Show Context)