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Including the Size of Regions in image segmentation by Region-based graph

IEEE TRANSACTIONS ON image PROCESSING 2014年第2期23卷 635-644页

作者： Rezvanifar, Alireza Khosravifard, Mohammadali Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran

Applying a fast over-segmentation algorithm to image and working on a region-based graph (instead of the pixel-based graph) is an efficient approach to reduce the computational complexity of graph-based image segmentation methods. Nevertheless, some undesirable effects may arise if the conventional cost functions, such as Ncut, AverageCut, and MinCut, are employed for partitioning the region-based graph. This is because these cost functions are generally tailored to pixel-based graphs. In order to resolve this problem, we first introduce a new class of cost functions (containing Ncut and AverageCut) for graph partitioning whose corresponding suboptimal solution can be efficiently computed by solving a generalized eigenvalue problem. Then, among these cost functions, we propose one that considers the size of regions in the partitioning procedure. By simulation, the performance of the proposed cost function is quantitatively compared with that of the Ncut and AverageCut.

关键词： graph-based image segmentation Ncut AverageCut spectral clustering region-based graph mean-shift algorithm

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Links Between image segmentation based on Optimum-Path Forest and Minimum Cut in graph

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JOURNAL OF MATHEMATICAL IMAGING AND VISION 2009年第2期35卷 128-142页

作者： Miranda, Paulo A. V. Falcao, Alexandre X. Univ Estadual Campinas Inst Comp LIV BR-13083970 Campinas SP Brazil

image segmentation can be elegantly solved by optimum-path forest and minimum cut in graph. Given that both approaches exploit similar image graphs, some comparative analysis is expected between them. We clarify their differences and provide their comparative analysis from the theoretical point of view, for the case of binary segmentation (object/background) in which hard constraints (seeds) are provided interactively. Particularly, we formally prove that some optimum-path forest methods from two distinct region-based segmentation paradigms, with internal and external seeds and with only internal seeds, indeed minimize some graph-cut measures. This leads to a proof of the necessary conditions under which the optimum-path forest algorithm and the min-cut/max-flow algorithm produce exactly the same segmentation result, allowing a comparative analysis between them.

关键词： image segmentation based on optimum-path forest graph-cut segmentation image foresting transform graph-based image segmentation

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Shape-based image segmentation through photometric stereo

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COMPUTER VISION AND image UNDERSTANDING 2011年第1期115卷 91-104页

作者： Julia, Carme Moreno, Rodrigo Puig, Domenec Angel Garcia, Miguel Univ Rovira & Virgili Intelligent Robot & Comp Vis Grp Dept Comp Sci & Math Tarragona 43007 Spain Univ Autonoma Madrid Dept Informat Engn E-28049 Madrid Spain

This paper describes a new algorithm for segmenting 2D images by taking into account 3D shape information. The proposed approach consists of two stages. In the first stage, the 3D surface normals of the objects present in the scene are estimated through robust photometric stereo. Then, the image is segmented by grouping its pixels according to their estimated normals through graph-based clustering. One of the advantages of the proposed approach is that, although the segmentation is based on the 3D shape of the objects, the photometric stereo stage used to estimate the 3D normals only requires a set of 2D images. This paper provides an extensive validation of the proposed approach by comparing it with several image segmentation algorithms. Particularly, it is compared with both appearance-based image segmentation algorithms and shape-based ones. Experimental results confirm that the latter are more suitable when the objective is to segment the objects or surfaces present in the scene. Moreover, results show that the proposed approach yields the best image segmentation in most of the cases. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Photometric stereo 3D surface normals graph-based image segmentation

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IFTrace: Video segmentation of deformable objects using the image Foresting Transform

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COMPUTER VISION AND image UNDERSTANDING 2012年第2期116卷 274-291页

作者： Minetto, R. Spina, T. V. Falcao, A. X. Leite, N. J. Papa, J. P. Stolfi, J. Univ Estadual Campinas UNICAMP Inst Comp Campinas SP Brazil Univ Estadual Paulista Dept Comp Bauru Brazil

We introduce IFTrace, a method for video segmentation of deformable objects. The algorithm makes minimal assumptions about the nature of the tracked object: basically, that it consists of a few connected regions, and has a well-defined border. The objects to be tracked are interactively segmented in the first frame of the video, and a set of markers is then automatically selected in the interior and immediate surroundings of the object. These markers are then located in the next frame by a combination of KLT feature finding and motion extrapolation. Object boundaries are then identified from these markers by the image Foresting Transform (IFT). These steps are repeated for all subsequent frames until the end of the movie. Thanks to the IFT and a special boundary detection operator, IFTrace can reliably track deformable objects in the presence of partial and total occlusions, camera motion, lighting and color changes, and other complications. Tests on real videos show that the IFT is better suited to this task than graph-Cut methods, and that IFTrace is more robust than other state-of-the art algorithms - namely, the OpenCV Snake and Cam-Shift algorithms, Hess's Particle-Filter, and Zhong and Chang's method based on spatio-temporal consistency. (C) 2011 Elsevier Inc. All rights reserved.

关键词： segmentation/tracking of moving objects Object delineation image/video segmentation image Foresting Transform graph-based image segmentation

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FAST INTERACTIVE segmentation OF NATURAL imageS USING THE image FORESTING TRANSFORM

FAST INTERACTIVE SEGMENTATION OF NATURAL IMAGES USING THE IM...

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16th International Conference on Digital Signal Processing

作者： Spina, T. V. Montoya-Zegarra, Javier A. Falcao, A. X. Miranda, P. A. V. Univ Estadual Campinas Inst Comp BR-13084971 Campinas SP Brazil

ISBN: (纸本)9781424432974

This paper presents an unified framework for fast interactive segmentation of natural images using the image foresting transform (IFT) - a tool for the design of image processing operators based on connectivity functions (path-value functions) in graphs derived from the image. It mainly consists of three tasks: recognition, enhancement, and extraction. Recognition is the only interactive task, where representative image properties for enhancement and the object's location for extraction are indicated by drawing a few markers in the image. Enhancement increases the dissimilarities between object and background for more effective object extraction, which completes segmentation. We show through extensive experiments that, by exploiting the synergism between user and computer for recognition and enhancement, respectively, as a separated step from recognition and extraction, respectively, one can reduce user involvement with better accuracy. We also describe new methods for enhancement based on fuzzy classification by IFT and for feature selection and/or combination by genetic programming.

关键词： graph-based image segmentation image feature selection and/or combination genetic programming fuzzy classification differential image foresting transform multiscale image filtering

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A comparative study among pattern classifiers in interactive image segmentation

A comparative study among pattern classifiers in interactive...

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22nd Brazilian Symposium on Computer graphics and image Processing

作者： Spina, Thiago V. Montoya-Zegarra, Javier A. Andrijauskas, Fabio Faria, Fabio A. Zampieri, Carlos E. A. Pinto-Caceres, Sheila M. de Carvalho, Tiago J. Falcao, Alexandre X. Univ Campinas UNICAMP Inst Comp BR-13084971 Campinas SP Brazil

ISBN: (纸本)9781424449781

Edition of natural images usually asks for considerable user involvement, being segmentation one of the main challenges. This paper describes an unified graph-based framework for fast, precise and accurate interactive image segmentation. The method divides segmentation into object recognition, enhancement and extraction. Recognition is done by the user when markers are selected inside and outside the object. Enhancement increases the dissimilarities between object and background and Extraction separates them. Enhancement is done by a fuzzy pixel classifier and it has a great impact in the number of markers required for extraction. In view of minimizing user involvement, we focus this paper on a comparative study among popular classifiers for enhancement, conducting experiments with several natural images and seven users.

关键词： Pattern classifiers graph-based image segmentation image foresting transform fuzzy classification

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Towards Interactive image segmentation by Dynamic and Iterative Spanning Forest 1st

Towards Interactive Image Segmentation by Dynamic and Iterat...

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1st IAPR International Joint Conference on Discrete Geometry and Mathematical Morphology (DGMM)

作者： Barcelos, Isabela Borlido Belem, Felipe Miranda, Paulo Falcao, Alexandre Xavier do Patrocinio, Zenilton K. G., Jr. Guimaraes, Silvio Jamil F. Pontificia Univ Catolica Minas Gerais Lab Image & Multimedia Data Sci ImSci BR-31980110 Belo Horizonte Brazil Univ Estadual Campinas Lab Image Data Sci LIDS BR-13083852 Sao Paulo Brazil Univ Estadual Campinas Inst Math & Stat BR-05508090 Sao Paulo Brazil

ISBN: (纸本)9783030766566;9783030766573

Interactive image segmentation aims to partition the image into background and foreground objects by taking into account seeds inserted by users. Nowadays, many methods are capable of generating segmentations with few user interactions, especially region-based techniques. However, such methods are highly sensitive to seed displacement and quantity, and delineation errors are often propagated to the final segmentation result. Recently, a novel superpixel segmentation framework, named Dynamic and Iterative Spanning Forest (DISF), was proposed, which achieved top delineation performance while assessing many seed-based state-of-the-art methods' drawbacks. In this work, we propose interactive DISF (iDISF), an interactive segmentation framework, by modifying each step of DISF to consider user-validated information. DISF uses the image Foresting Transform (IFT) framework for computing an optimum-path forest rooted in a seed set in the delineation step. To consider path and image gradient variation, we propose three new connectivity functions for the IFT. Finally, we also propose two new seed removal strategies for detecting relevant seeds for subsequent iterations. Results show segmentation improvements for minimal user effort-i.e., a single click-and show theoretical advances that may benefit recent optimum-path-based interactive methods from scribbles.

关键词： graph-based image segmentation Dynamic and iterative spanning forest Interactive segmentation

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Grab Cut image segmentation based on image Region 3

Grab Cut Image Segmentation Based on Image Region

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3rd IEEE International Conference on image, Vision and Computing (ICIVC)

作者： Li, Yubing Zhang, Jinbo Gao, Peng Jiang, Liangcheng Chen, Ming Southeast Univ Natl Mobile Commun Res Lab Nanjing 211111 Jiangsu Peoples R China

ISBN: (纸本)9781538649916

Grab Cut algorithm is one of the most popular method in the field of image segmentation. It uses texture infor- mation and boundary information of image, and achieves good segmentation results with a small number of user interaction. But there are two significant drawbacks about this algorithm. Firstly, If the background is complex or the background and the object are very similar, the segmentation will not be very good. On the other hand, the relatively slow speed and Complex iterative process of the algorithm are greatly limited its application. In this paper, to develop these aspects, we proposed an improved grab cut algorithm. This algorithm is the combination of grab cut and graph-based image segmentation [1]. After the experiment, the improved algorithm is applied to more complex situation.

关键词： image segmentation grab cut graph-based image segmentation

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image segmentation using dense and sparse hierarchies of superpixels

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PATTERN RECOGNITION 2020年 108卷 107532-107532页

作者： Galvao, Felipe Lemes Guimaraes, Silvio Jamil Ferzoli Falcao, Alexandre Xavier Univ Estadual Campinas Inst Comp Lab Image Data Sci Av Albert Einstein 1251 BR-13083852 Campinas SP Brazil Pontificia Univ Catolica Minas Gerais Comp Sci Dept BR-31980110 Belo Horizonte MG Brazil

We investigate the intersection between hierarchical and superpixel image segmentation. Two strategies are considered: (i) the classical region merging, that creates a dense hierarchy with a higher number of levels, and (ii) the recursive execution of some superpixel algorithm, which generates a sparse hierarchy with fewer levels. We show that, while dense methods can capture more intermediate or higher-level object information, sparse methods are considerably faster and usually with higher boundary adherence at finer levels. We first formalize the two strategies and present a sparse method, which is faster than its superpixel algorithm and with similar boundary adherence. We then propose a new dense method to be used as post-processing from the intermediate level, as obtained by our sparse method, upwards. This combination results in a unique strategy and the most effective hierarchical segmentation method among the compared state-of-the-art approaches, with efficiency comparable to the fastest superpixel algorithms. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Superpixel segmentation Hierarchical image segmentation image foresting transform Iterative spanning forest graph-based image segmentation Irregular image pyramid

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INTELLIGENT UNDERSTANDING OF USER INTERACTION IN image segmentation

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2012年第2期26卷 1265001-1265001页

作者： Spina, Thiago V. De Miranda, Paulo A. V. Falcao, Alexandre X. Univ Campinas UNICAMP Inst Comp Campinas SP Brazil Univ Sao Paulo Inst Math & Stat Dept Comp Sci Sao Paulo Brazil

We have developed interactive tools for graph-based segmentation of natural images, in which the user guides object delineation by drawing strokes (markers) inside and outside the object. A suitable arc-weight estimation is paramount to minimize user time and maximize segmentation accuracy in these tools. However, it depends on discriminative image properties for object and background. These properties can be obtained from some marker pixels, but their identification is a hard problemduring delineation. Careless arc-weight re-estimation reduces user control and drops performance, while interactive arc-weight estimation in a step before interactive object extraction is the best option so far, albeit it is not intuitive for nonexpert users. We present an effective solution using the unified framework of the image foresting transform (IFT) with three operators: clustering for interpreting user interaction and determining when and where arc weights need to be re-estimated;fuzzy classification for arc-weight estimation;and marker competition based on optimum connectivity for object extraction. For validation, we compared the proposed approach with another interactive IFT-based method, which computes arc weights before extraction. Evaluation involved multiple users (experts and nonexperts), a dataset with several natural images, and measurements to quantify accuracy, precision, efficiency (user time and computation time), and user control, being some of them novel measurements, proposed in this work.

关键词： graph-based image segmentation intelligent arc-weight estimation image foresting transform fuzzy classification clustering

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