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unsupervised segmentation of multitemporal interferometric SAR images

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1999年第5期37卷 2259-2271页

作者： Dammert, PBG Askne, JIH Kühlmann, S Chalmers Univ Technol Dept Radio & Space Sci S-41296 Gothenburg Sweden Chalmers Univ Technol Dept Math Stat S-41296 Gothenburg Sweden

This paper shows how to segment large data sets of multitemporal and interferometric SAR images usi ng an unsupervised, fuzzy clustering method. An adaptive feature extraction (principal component transformation) is employed which may drastically reduce the number of images and improves the final results. This also speeds up the fuzzy clustering iteration part considerably, The method is applied to data over two areas in Sweden: one typical urban area with forest and farmland surroundings and a forested area. The best classification accuracy is obtained when classifying the data into two classes, agreeing with the predictions of the cluster validity parameters used in this study. The method always finds the dominating land-covers in the images first. These are then subdivided as more clusters (classes) are identified, indicating that the segmentation is moderately hierarchical. The final classification results, between 65% and 75%, are comparable to those obtained in other studies. Analyzing the final cluster signatures reveals that the current unsupervised method has several similarities with rule-based methods.

关键词： classification fuzzy clustering multitemporal SAR SAR interferometry unsupervised image segmentation

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segmentation of color images via reversible jump MCMC sampling

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image AND VISION COMPUTING 2008年第3期26卷 361-371页

作者： Kato, Zoltan Univ Szeged Inst Informat H-6701 Szeged Hungary

Reversible jump Markov chain Monte Carlo (RJMCMC) is a recent method which makes it possible to construct reversible Markov chain samplers that jump between parameter subspaces of different dimensionality. In this paper, we propose a new RJMCMC sampler for multivariate Gaussian mixture identification and we apply it to color image segmentation. For this purpose, we consider a first order Markov random field (MRF) model where the singleton energies derive from a multivariate Gaussian distribution and second order potentials favor similar classes in neighboring pixels. The proposed algorithm finds the most likely number of classes, their associated model parameters and generates a segmentation of the image by classifying the pixels into these classes. The estimation is done according to the Maximum A Posteriori (MAP) criterion. The algorithm has been validated on a database of real images with human segmented ground truth. (c) 2006 Elsevier B.V. All rights reserved.

关键词： unsupervised image segmentation color parameter estimation normal mixture identification Markov random fields reversible jump Markov chain Monte Carlo simulated annealing

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unsupervised scene segmentation using sparse coding context

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MACHINE VISION AND APPLICATIONS 2013年第2期24卷 243-254页

作者： Liu, Yen-Cheng Chen, Hwann-Tzong Natl Tsing Hua Univ Dept Comp Sci Hsinchu 30043 Taiwan

This paper presents an approach to image understanding on the aspect of unsupervised scene segmentation. With the goal of image understanding in mind, we consider 'unsupervised scene segmentation' a task of dividing a given image into semantically meaningful regions without using annotation or other human-labeled information. We seek to investigate how well an algorithm can achieve at partitioning an image with limited human-involved learning procedures. Specifically, we are interested in developing an unsupervised segmentation algorithm that only relies on the contextual prior learned from a set of images. Our algorithm incorporates a small set of images that are similar to the input image in their scene structures. We use the sparse coding technique to analyze the appearance of this set of images;the effectiveness of sparse coding allows us to derive a priori the context of the scene from the set of images. Gaussian mixture models can then be constructed for different parts of the input image based on the sparse-coding contextual prior, and can be combined into an Markov-random-field-based segmentation process. The experimental results show that our unsupervised segmentation algorithm is able to partition an image into semantic regions, such as buildings, roads, trees, and skies, without using human-annotated information. The semantic regions generated by our algorithm can be useful, as pre-processed inputs for subsequent classification-based labeling algorithms, in achieving automatic scene annotation and scene parsing.

关键词： unsupervised image segmentation Semantic scene analysis Sparse coding Markov random fields

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Sub-scene segmentation using constraints based on Gestalt principles

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JOURNAL OF VISUAL COMMUNICATION AND image REPRESENTATION 2014年第5期25卷 994-1005页

作者： Zhu, Shan-shan Yung, Nelson H. C. Univ Hong Kong Dept Elect & Elect Engn Lab Intelligent Transportat Syst Res Hong Kong Hong Kong Peoples R China

In this paper, an unsupervised sub-scene segmentation method is proposed. It emphasizes on generating more integrated and semantically consistent regions instead of homogeneous but detailed over-segmented regions usually produced by conventional segmentation methods. Several properties of sub-scenes are explored such as proximity grouping, area of influence, similarity and harmony based on psychological principles. These properties are formulated into constraints that are used directly in the proposed sub-scene segmentation. A self-determined approach is conducted to get the optimal segmentation result based on the characteristics of each image in an unsupervised manner. The proposed method is evaluated over three datasets. For quantitative evaluation, the performance of the proposed method is on par with state-of-the-art unsupervised segmentation methods;for qualitative evaluation, the proposed method handles various sub-scenes well, and produces neater results. The sub-scenes segmented by the proposed method are generally consistent with natural scene categories. (c) 2014 Elsevier Inc. All rights reserved.

关键词： unsupervised image segmentation Semantic gap Gestalt principles Proximity grouping Pattern clustering Sub-scene segmentation Sub-scene property Mahalanobis distance

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A BYY scale-incremental EM algorithm for Gaussian mixture learning

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APPLIED MATHEMATICS AND COMPUTATION 2008年第2期205卷 832-840页

作者： Li, Lei Ma, Jinwen Peking Univ Sch Math Sci Dept Informat Sci Beijing 100871 Peoples R China Peking Univ LMAM Beijing 100871 Peoples R China

Gaussian mixture model has been used extensively in the fields of information processing and data analysis. However, its model selection, i.e., the selection of number of components or Gaussians in the mixture, is still a difficult problem. Fortunately, the new established Bayesian Ying-Yang (BYY) harmony function provides an efficient criterion for the model selection of Gaussian mixture with a set of sample data. In this paper, we propose a BYY scale-incremental EM algorithm for Gaussian mixture learning via a component split rule to increase the BYY harmony function incrementally. Particularly, starting from two components and adding one component sequentially via the split rule after each EM procedure until a maximum number of components, the algorithm increases the scale of the mixture incrementally and leads to the maximization of the BYY harmony function, together with the correct model selection and a good parameter estimation of the Gaussian mixture. It is demonstrated well by the simulation experiments that this BYY scale-incremental EM algorithm can make both model selection and parameter estimation efficiently for Gaussian mixture modeling. Moreover, the BYY scale-incremental EM algorithm is successfully applied to two real-life data sets, including Iris data classification and unsupervised color image segmentation. (C) 2008 Elsevier Inc. All rights reserved.

关键词： Bayesian Ying-Yang (BYY) harmony learning Gaussian mixture EM algorithm Model selection unsupervised image segmentation

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unsupervised segmentation of new semi-Markov chains hidden with long dependence noise

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SIGNAL PROCESSING 2010年第11期90卷 2899-2910页

作者： Lapuyade-Lahorgue, Jerome Pieczynski, Wojciech Telecom Sudparis Inst Telecom CITI Dept CNRSUMR 5157 F-91000 Evry France

The hidden Markov chain (HMC) model is a couple of random sequences (X,Y), in which X is an unobservable Markov chain, and Y is its observable "noisy version". The chain X is a Markov one and the components of Y are independent conditionally on X. Such a model can be extended in two directions: (i) X is a semi-Markov chain and (ii) the distribution of Y conditionally on X is a "long dependence" one. Until now these two extensions have been considered separately and the contribution of this paper is to consider them simultaneously. A new "semi-Markov chain hidden with long dependence noise" model is proposed and it is specified how it can be used to recover X from Y in an unsupervised manner. In addition, a new family of semi-Markov chains is proposed. Its advantages with respect to the classical formulations are the low computer time needed to perform different classical computations and the facility of its parameter estimation. Some experiments showing the interest of this new semi-Markov chain hidden with long dependence noise are also provided. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Hidden semi-Markov chains unsupervised signal segmentation Long dependence noise Iterative conditional estimation unsupervised signal segmentation unsupervised image segmentation

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unsupervised segmentation of natural images based on statistical modeling

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NEUROCOMPUTING 2017年 252卷 95-101页

作者： Zhu, Zhong-jie Wang, Yu-er Jiang, Gang-yi Zhejiang Wanli Univ Ningbo Key Lab DSP Ningbo 315100 Zhejiang Peoples R China Ningbo Univ Inst Circuits & Syst Ningbo 315211 Zhejiang Peoples R China

A novel unsupervised scheme for natural image segmentation is proposed aiming to acquire perceptually consistent results. Firstly, comprehensive visual features besides raw color values are extracted, including spatial frequency, contrast sensitivity, color deviation, and so on. Secondly, high correlations among visual features are reduced via principal component analysis (PCA) and the raw image pixels are then converted to a collection of feature vectors in a multi-dimensional feature space. Thirdly, the Gaussian mixture model (GMM) is employed to approximate the class distribution of image pixels and an improved, expectation maximization (EM) algorithm is introduced to estimate model parameters. Finally, segmentation results are obtained by grouping of pixels based on the mixture components. Experiments are conducted and the results demonstrate that, compared with existing techniques, the proposed scheme can acquire more perceptually consistent results. (C) 2017 Elsevier B.V. All rights reserved.

关键词： unsupervised image segmentation Visual feature PCA Statistical modeling Improved EM algorithm

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MDS-based segmentation model for the fusion of contour and texture cues in natural images

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COMPUTER VISION AND image UNDERSTANDING 2012年第9期116卷 981-990页

作者： Mignotte, Max Univ Montreal DIRO Montreal PQ H3C 3J7 Canada

In this paper, we present an original image segmentation model based on a preliminary spatially adaptive non-linear data dimensionality reduction step integrating contour and texture cues. This new dimensionality reduction model aims at converting an input texture image into a noisy color image in order to greatly simplify its subsequent segmentation. In this latter de-texturing model, the (spatially adaptive) non-local constraints based on edge and contour cues allows us to efficiently regularize the reduced data (or the resulting de-textured color image) and to efficiently combine inhomogeneous region and edge based features in a data fusion/reduction model used as pre-processing step for a final segmentation task. In addition, a set of color/texture and edge-based adaptive spatial continuity constraints is imposed during the segmentation step. These improvements lead to an appealing and powerful two-step adaptive segmentation model, integrating contour and texture cues. Extensive experimental evaluation on the Berkeley image segmentation database demonstrates the efficiency of this hybrid segmentation model in terms of classification accuracy of pairwise pixels in the resulting segmentation map and in the precision-recall framework widespread used for evaluating contour detectors. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Color textured image Berkeley image database Multidimensional scaling (MDS) Non-linear dimensionality reduction unsupervised image segmentation Probability Rand Index (PRI) F-measure Precision-recall framework

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The BYY annealing learning algorithm for Gaussian mixture with automated model selection

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PATTERN RECOGNITION 2007年第7期40卷 2029-2037页

作者： Ma, Jinwen Liu, Jianfeng Peking Univ Sch Math Sci Dept Informat Sci Beijing 100871 Peoples R China Peking Univ LMAM Beijing 100871 Peoples R China

Bayesian Ying-Yang (BYY) learning has provided a new mechanism that makes parameter learning with automated model selection via maximizing a harmony function on a backward architecture of the BYY system for the Gaussian mixture. However, since there are a large number of local maxima for the harmony function, any local searching algorithm, such as the hard-cut EM algorithm, does not work well. In order to overcome this difficulty, we propose a simulated annealing learning algorithm to search the global maximum of the harmony function, being expressed as a kind of deterministic annealing EM procedure. It is demonstrated by the simulation experiments that this BYY annealing learning algorithm can efficiently and automatically determine the number of clusters or Gaussians during the learning process. Moreover, the BYY annealing learning algorithm is successfully applied to two real-life data sets, including Iris data classification and unsupervised color image segmentation. (c) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： Bayesian Ying-Yang (BYY) learning Gaussian mixture automated model selection simulated annealing unsupervised image segmentation

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unsupervised segmentation of multiview feature semantics by hashing model

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SIGNAL PROCESSING 2019年 160卷 106-112页

作者： Cui, Jia Zhao, Yanhui Dong, Xinfeng Tang, Mingxi Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China Shaanxi Fash Engn Univ Xian 712046 Shaanxi Peoples R China

The image segmentation of finely detailed object in images is becoming increasingly important in various image applications. Herein, multiview features such as color, spatial features, texture, saliency, and depth are used to calculate the feature semantic that differentiates the image foreground and background at the pixel level. For different image type, the importance of every single image feature is various. We assume that combining multiple features will produce improved results compared to using any single feature and propose an unsupervised learning strategy to learn optimal feature projection to the final solution surface of binary segmentation. To reduce computational complexity, a hashing model is used to represent the projected feature using binary codes, as its hamming distance efficiently retrieves the pixel similarity. Comparative experiments using the current state-of-the-art unsupervised segmentation algorithms and a deep learning approach, i.e., a fully convolutional network, demonstrate the advantages of the proposed method. (C) 2019 Published by Elsevier B.V.

关键词： unsupervised image segmentation Multiview feature projection Similarity distance Hashing model

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