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unsupervised texture segmentation using resonance algorithm for natural scenes

PATTERN RECOGNITION LETTERS 2000年第8期21卷 741-757页

作者： He, H Chen, YQ Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Many texture segmentation methods in the literature assume that the changes of intensity can be ascribed to the texture themselves. However, the real-world images may contain wide-ranged gradations in intensity which have nothing to do with local texture, such as those caused by the environment illuminations and cameras. To overcome the problem, an unsupervised texture segmentation method is proposed in this paper. Emphasizing the spatial relations between the adjacent texture pixels, the algorithm begins from a set of seed pixels and the texture region is generated by including those similar pixels. To suppress the noise influence, special attention is paid to the similarity criterion. Furthermore, to meet the requirement of unsupervised segmentation, the threshold in the similarity checking is automatically determined via iteratively applying the algorithm. The experimental results on Brodatz texture images and real-world images are presented. (C) 2000 Published by Elsevier Science B.V. All rights reserved.

关键词： unsupervised texture segmentation resonance algorithm natural scenes threshold auto-detection

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unsupervised texture segmentation of Natural Scene Images Using Region-based Markov Random Field

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2016年第3期83卷 423-436页

作者： O, Na Kyoung Kim, Changick Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305701 South Korea

In analyzing natural scene images, texture plays an important role because such images are full of various textures. Although texture is crucial information in analyzing natural scene images, the texture segmentation problem is still hard to solve since the texture often exhibit non-uniform statistical characteristics. Although there are several supervised approaches that partition an image according to pre-defined semantic categories, the ever-changing appearances in the natural images make such schemes intractable. To overcome this limitation, we propose a novel unsupervised texture segmentation method for natural images by using the Region-based Markov Random Field (RMRF) model which enforces the spatial coherence between neighbor regions. We introduce the concept of pivot regions which plays a decisive role to incorporate local data interaction. By forcing pivot regions to adhere to initial labels, we make the Markov Random Field evolve fast and precisely. The proposed algorithm based on the pivot regions and the MRF for encapsulating spatial dependencies between neighborhoods yields high performance for the unsupervised segmentation of natural scene images. Quantitative and qualitative evaluations prove that the proposed method achieves comparable results with other algorithms.

关键词： unsupervised texture segmentation Markov random field Natural scene images

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unsupervised texture segmentation via wavelet transform

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PATTERN RECOGNITION 1997年第5期30卷 729-742页

作者： Lu, CS Chung, PC Chen, CF NATL CHENG KUNG UNIV DEPT ELECT ENGNTAINAN 70101TAIWAN BOSTON UNIV DEPT APPL SCIBOSTONMA 02215

In this paper, a mechanism for unsupervised texture segmentation is presented. The approach is based on the multiscale representation of the discrete (dyadic) wavelet transform which can be implemented by a fast iterative algorithm. For unsupervised segmentation it is generally difficult to determine the number of classes to be identified. The proposed approach offers an approach to circumvent this problem. Our method utilizes a set of high-frequency channel energies to characterize texture features, followed by a multi-thresholding technique for coarse segmentation. The coarsely segmented results at the same scale are incorporated by an intra-scale fusion procedure. A fine segmentation technique is then used to reclassify the ambiguously labeled pixels generated from the intra-scale fusion step. Finally, the number of texture classes is determined by an inter-scale fusion in which the segmentation results at multiple scales are integrated. The performance of this method is demonstrated by several experiments on synthetic images, natural textures from Brodatz's album and real-world textured images. Since the choice of wavelets is very extensive and open, we further explore various types of wavelets for texture segmentation. The time cost of the proposed method is also measured. (C) 1997 Pattern Recognition Society.

关键词： unsupervised texture segmentation wavelet transform multiscale representation multithresholding coarse segmentation fusion fine segmentation

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Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets

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IET IMAGE PROCESSING 2018年第9期12卷 1626-1638页

作者： Huang, Yuan De Bortoli, Valentin Zhou, Fugen Gilles, Jerome Beihang Univ Image Proc Ctr Beijing 100191 Peoples R China Ecole Normale Super Paris Saclay Ctr Math & Leurs Applicat 61 Ave President Wilson F-94230 Cachan France San Diego State Univ Dept Math & Stat 5500 Campanile Dr San Diego CA 92182 USA

Wavelet-based segmentation approaches are widely used for texture segmentation purposes because of their ability to characterise different textures. In this study, the authors assess the influence of the chosen wavelet and propose to use the recently introduced empirical wavelets. We show that the adaptability of the empirical wavelet permits to reach better results than classic wavelets. To focus only on the textural information, they also propose to perform a cartoon+texture decomposition step before applying the segmentation algorithm. The proposed method is tested on six classic benchmarks, based on several popular texture images.

关键词： image segmentation image texture wavelet transforms unsupervised texture segmentation adaptive wavelets texture segmentation purposes adaptability empirical wavelet classic wavelets textural information cartoon plus texture decomposition step texture images

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Fast unsupervised texture segmentation using Texel similarity map

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JOURNAL OF MODERN OPTICS 2015年第15期62卷 1211-1222页

作者： Wang, Xiaoyan Yang, Minghao Xia, Ming Zhan, Yongsong Shi, Lihui Ma, Chuanyan Tao, Jianhua Chen, Shengyong Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin Peoples R China Beijing Inst Special Electrometr Technol Beijing Peoples R China

unsupervised texture segmentation is a challenging topic in computer vision. It is difficult to obtain boundaries of texture regions automatically in real-time, especially for cluttered images. This paper presents a new fast unsupervised texture segmentation method. First, the Texel similarity map (TSM) is used to compare the changes of intensity and gray level of neighboring pixels to determine whether they are identical. Then, a scheme called multiple directions integral images (MDII) is proposed to quickly evaluate the TSM. With the aid of MDII, one pixel's similarity value can be computed in constant time. Finally, the proposed segmentation method is tested on both artificial texture and natural images. Experimental results show that the proposed method performs well on a wide range of images, and outperforms state-of-the-art method on segmentation speed.

关键词： unsupervised texture segmentation Texel similarity map multiple directions integral images image processing

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A novel parallel unsupervised texture segmentation approach

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SN APPLIED SCIENCES 2023年第6期5卷 1-12页

作者： Ge, Guangtao Zhejiang Gongshang Univ Sussex Artificial Intelligence Inst Sch Informat & Elect Engn Hangzhou 310018 Peoples R China

This study presents a new idea for unsupervised texture segmentation from the angle of bionics. According to this idea, a novel approach called "side-by-side superposition unsupervised texture segmentation (SSUTS)" is designed. When doing texture segmentation in this paper, the traditional time-frequency analysis-based methods are improved by introducing the analytic phase theory and the Bi-dimensional Empirical mode decomposition (BEMD) theory. Experiments prove that the SSUTS approach is effective in texture segmentation of different styles.

关键词： unsupervised texture segmentation Time-frequency analysis The analytic phase Bi-dimensional Empirical Mode Decomposition (BEMD)

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Snake based unsupervised texture segmentation using Gaussian Markov Random Field Models

Snake based Unsupervised Texture Segmentation using Gaussian...

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18th IEEE International Conference on Image Processing (ICIP)

作者： Mahmoodi, Sasan Gunn, Steve Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

ISBN: (纸本)9781457713033

A functional for unsupervised texture segmentation is investigated in this paper. An auto-normal model based on Markov Random Fields is employed here to represent textures. The functional investigated here is optimized with respect to the auto-normal model parameters and the evolving contour to simultaneously estimate auto-normal model parameters and find the evolving contour. Experimental results applied on the textures of the Brodatz album demonstrate the higher speed of convergence of this algorithm in comparison with a traditional stochastic algorithm in the literature.

关键词： unsupervised texture segmentation Gaussian Markov Random Field Model Mumford-Shah Model Auto-normal Model Maximum Likelihood

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A DATA-CENTRIC APPROACH TO unsupervised texture segmentation USING PRINCIPLE REPRESENTATIVE PATTERNS 44

A DATA-CENTRIC APPROACH TO UNSUPERVISED TEXTURE SEGMENTATION...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Zhang, Kaitai Chen, Hong-Shuo Zhang, Xinfeng Wang, Ye Kuo, C. -C. Jay Univ Southern Calif Ming Heish Dept Elect Engn Los Angeles CA 90007 USA Natl Chiao Tung Univ Coll Elect & Comp Engn Hsinchu Taiwan

ISBN: (纸本)9781479981311

Features that capture textural patterns of a certain class of images are crucial for texture segmentation tasks. This paper introduces a data-centric approach to efficiently extract and represent textural information, which adapts to a wide variety of textures. Based on the strong self-similarities and quasi periodicity in texture images, the proposed method first constructs a representative texture pattern set for the given image by leveraging the patch clustering strategy. Then, pixel wise texture features are designed according to the similarities between local patches and the representative textural patterns. Moreover, the proposed feature is generic and flexible, and can perform segmentation task by integrating it into various segmentation approaches easily. Extensive experimental results on both textural and natural image segmentation show that the segmentation method using the proposed features achieves very competitive or even better performance compared with the stat-of-the-art methods.

关键词： unsupervised texture segmentation Data-centric feature extraction self-similarity

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unsupervised texture-based image segmentation through pattern discovery

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COMPUTER VISION AND IMAGE UNDERSTANDING 2011年第8期115卷 1121-1133页

作者： Melendez, Jaime Angel Garcia, Miguel Puig, Domenec Petrou, Maria Univ Rovira & Virgili Dept Comp Sci & Math Intelligent Robot & Comp Vis Grp Tarragona 43007 Spain Autonomous Univ Madrid Dept Elect & Commun Technol E-28049 Madrid Spain Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn Commun & Signal Proc Grp London SW7 2AZ England

This paper presents a new efficient technique for unsupervised segmentation of textured images that aims at incorporating the advantages of supervision for discriminating texture patterns. First, a pattern discovery stage that relies on a clustering algorithm is utilized for determining the texture patterns of a given image based on the outcome of a multichannel Gabor filter bank. Then, a supervised pixel-based classifier trained with the feature vectors associated with those patterns is used to classify every image pixel into one of the sought texture classes, thus yielding the final segmentation. Multi-sized evaluation windows following a top-down approach are utilized during pixel classification in order to improve accuracy both inside and near boundaries of regions of homogeneous texture. Results with synthetic compositions and with complex real images are presented and discussed. The proposed technique is also compared with alternative texture segmentation approaches. (C) 2011 Elsevier Inc. All rights reserved.

关键词： unsupervised texture segmentation Supervised pixel-based texture classification Multi-sized evaluation windows Gabor filters

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A genetic algorithm for MRF-based segmentation of multi-spectral textured images

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PATTERN RECOGNITION LETTERS 1999年第14期20卷 1499-1510页

作者： Tseng, DC Lai, CC Natl Cent Univ Inst Comp Sci & Informat Engn Chungli 320 Taiwan

A segmentation approach based on a Markov random field (MRF) model is an iterative algorithm;it needs many iteration steps to approximate a near optimal solution or gets a non-suitable solution with a few iteration steps. Tn this paper, we use a genetic algorithm (GA) to improve an unsupervised MRF-based segmentation approach for multispectral textured images. The proposed hybrid approach has the advantage that combines the fast convergence of the MRF-based iterative algorithm and the powerful global exploration of the GA. In experiments, synthesized color textured images and multi-spectral remote-sensing images were processed by the proposed approach to evaluate the segmentation performance. The experimental results reveal that the proposed approach really improves the MRF-based segmentation for the multi-spectral textured images. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： unsupervised texture segmentation Markov random field genetic algorithm multi-spectral remote-sensing images

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