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Ensemble CART surrogate-assisted automatic multi-objective rough fuzzy clustering algorithm for unsupervised image segmentation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 133卷

作者： Zhao, Feng Tang, Zihan Xiao, Zhilei Liu, Hanqiang Fan, Jiulun Li, Lu Xian Univ Posts & Telecommun Sch Telecommun & Informat Engn Xian 710121 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Univ Posts & Telecommun Sch Automat Changan West St Changan Dist Xian 710121 Peoples R China

Multi-objective clustering algorithms (MOCAs) are popular in unsupervised image segmentation due to their merit of meeting multiple segmentation requirements and the prospect of automatically estimating the number of clusters. However, most of them suffer from high time costs and are easily to be influenced by the uncertainty when handling real complex images. To address these issues, we propose an ensemble classification and regression tree (CART) surrogate-assisted automatic multi-objective rough fuzzy clustering (ECS-AMRFC) algorithm for unsupervised image segmentation. Firstly, a cluster medoid-based encoding scheme is employed to represent solutions with different number of clusters and meanwhile lessen the length of encoding. Then, we design an ensemble CART as the surrogate model to significantly reduce the computational burden. Moreover, a surrogate model management strategy is proposed to accelerate the optimization and enhance the quality of surrogate modeling. To handle the uncertainty in data, we extend the rough fuzzy clustering into MOCAs and construct three complementary objective functions to seek proper cluster medoids from multiple perspectives. In addition, the Gaussian kernel is introduced into the objective functions to handle image pixels that cannot separate linearly in the feature space. Finally, a kernelized rough fuzzy clustering validity index is defined to automatically select the optimal solution with no requirements of any prior knowledge. Experiments show that ECS-AMRFC not only identifies appropriate number of clusters on different kinds of images, but also obtains better segmentation results than state-of-the-art rough fuzzy clustering algorithms and automatic MOCAs.

关键词： Automatic rough fuzzy clustering CART surrogate model Local search Multi-objective optimization unsupervised image segmentation

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Bottom-up unsupervised image segmentation using FC-Dense u-net based deep representation clustering and multidimensional feature fusion based region merging

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image AND VISION COMPUTING 2020年 94卷

作者： Khan, Zubair Yang, Jie Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China

Recent advances in system resources provide ease in the applicability of deep learning approaches in computer vision. In this paper, we propose a deep learning-based unsupervised image segmentation approach for natural image segmentation. image segmentation aims to transform an image into regions, representing various objects in the image. Our method consists of a fully convolutional dense network-based unsupervised deep representation oriented clustering, followed by shallow features based high-dimensional region merging to produce the final segmented image. We evaluate our proposed approach on the BSD300 database and perform a comparison with several classical and some recent deep learning-based unsupervised segmentation methods. The experimental results represent that the proposed method is comparable and confirm the efficacy of the proposed approach.

关键词： unsupervised image segmentation Deep learning Feature fusion Region merging image processing

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A competition in unsupervised color image segmentation

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PATTERN RECOGNITION 2016年 57卷 136-151页

作者： Haindl, Michal Mikes, Stanislav Acad Sci Czech Republ Inst Informat Theory & Automat CR-18208 Prague Czech Republic

A competition in unsupervised color image segmentation took place in conjunction with the 22nd International Conference on Pattern Recognition (ICPR 2014). It aimed to promote evaluation of unsupervised color image segmentation algorithms using publicly available data sets, and to allow for any subsequent methods to be easily evaluated and compared with the results of the contested methods under identical conditions. Our comparison of different methods is based on the standard methodology of performance assessment using an on-line verification server. We present in this paper the evaluation of the top six results submitted to the ICPR 2014 contest in unsupervised color image segmentation and compare them with 11 other state-of-the-art unsupervised image segmenters. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： unsupervised image segmentation segmentation contest Texture analysis

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unsupervised Natural image segmentation via Bayesian Ying-Yang Harmony Learning Theory

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NEUROCOMPUTING 2013年 121卷 532-539页

作者： Zhu, Shaojun Zhao, Jieyu Guo, Lijun Zhang, Yuanyuan Ningbo Univ Inst Comp Sci & Technol Ningbo 315211 Zhejiang Peoples R China

An unsupervised image segmentation method for natural images is proposed in this paper. We assume that texture features in natural images are distributed as a mixture of Gaussians. In order to cluster the extracted feature vectors, we modify a clustering algorithm based on Bayesian Ying-Yang (BYY) harmony learning theory with Dirichlet-Normal-Wishart prior. This algorithm can determine the number of components automatically during the clustering procedure, as long as we give a large enough initial component number. Our works in this paper have presented a complete pipeline of clustering-based image segmentation including feature extraction, robust feature clustering and methodological effective post processing. The experiments reported in this paper demonstrate that the proposed method is efficient (in terms of visual evaluation and quantitative performance measures) and performs competitively compared to the existing state-of-the-art segmentation methods. (C) 2013 Elsevier B.V. All rights reserved.

关键词： unsupervised image segmentation Bayesian Ying-Yang harmony learning Dirichlet-Normal-Wishart prior Superpixels Automatic model selection

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unsupervised color image segmentation with color-alone feature using region growing pulse coupled neural network

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NEUROCOMPUTING 2018年 306卷 1-16页

作者： Xu, Guangzhu Li, Xinyu Lei, Bangjun Lv, Ke China Three Gorges Univ Coll Comp & Informat Technol Yichang Peoples R China China Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hy Yichang Peoples R China Univ Chinese Acad Sci Sch Engn & Informat Technol Beijing Peoples R China

unsupervised color image segmentation based on low level color features aims to assign same label to all pixels of a region with color homogeneity, which underlies many higher level processing such as object detection and recognition. Pulse coupled neural network (PCNN) is a kind of biologically inspired spiking neural network, and has an inherent image segmentation nature which can combine each pixel's intensity and its spatial relationship with neighboring pixels well. But PCNN cannot deal with color images, which restricts its applications greatly. For the problem, this article studied the color information embedding into PCNN and presented an unsupervised color image segmentation algorithm based on the proposed color region growing PCNN (CRG-PCNN) model. First, RGB color space is converted into Lab in which color distance can be evaluated linearly. Then a linking control unit (LCN) is introduced which essentially is a switch triggered by assessing the color distance among PCNN neuron feeding inputs. When the color distance between two pixels is less than a predefined threshold, a linking between the corresponding neurons is established. By doing so, color information can be embedded into PCNN effectively. Next, the L channel of an input image is fed into CRG-PCNN which can automatically pick out a seed neuron in each iteration and facilitates the region growing continuously by modifying the linking coefficient linearly with the assistance of a fast linking mechanism until certain termination conditions are met. Four widely used quantitative indices for massive experiments conducted on Berkeley segmentation dataset verify the performance of the proposed method. It has good segmentation accuracy and is concise and intuitive. (C) 2018 Elsevier B.V. All rights reserved.

关键词： unsupervised image segmentation Color image PCNN Region growing

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INCORPORATING TEXTURE INFORMATION INTO REGION-BASED unsupervised image segmentation USING TEXTURAL SUPERPIXELS

INCORPORATING TEXTURE INFORMATION INTO REGION-BASED UNSUPERV...

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IEEE International Conference on image Processing

作者： Chih-Yu Hsu Yi-Yu Hsieh Kuo-Hua Lo Jen-Hui Chuang Inst. of Comput. Sci. & Eng. Nat. Chiao Tung Univ. Hsinchu Taiwan

ISBN: (纸本)9781479957521

Recently, an unsupervised image segmentation framework, segmentation by Aggregating Superpixels (SAS) is proposed and shown to be very promising. However, the texture cues, which have been shown to be very effective in many researches, are not used. In this paper, we propose an effective method for incorporating texture information into the SAS framework, using superpixels. To extract texture information, our algorithm first uses texture filtering and subsequently GMM clustering. Then, we develop an edge-aware low-pass filtering to generate multiple-scale textural superpixels (TXSPs) from the clustering results. Finally, by joining TXSPs with the superpixel set originally used in SAS, the incorporation of texture information is accomplished. Our method achieves superior performance on the Berkeley segmentation Dataset (BSDS300) under several evaluation criteria when compared to other benchmark algorithms.

关键词： Gaussian processes feature extraction filtering theory image resolution image segmentation image texture low-pass filters pattern clustering unsupervised learning BSDS300 Berkeley segmentation dataset GMM clustering SAS framework edge-aware low-pass filtering multiple-scale textural superpixel generation region-based unsupervised image segmentation framework segmentation-by-aggregating superpixels texture cues texture filtering texture information extraction Benchmark testing Computer vision Filter banks image segmentation Pattern recognition Synthetic aperture sonar Superpixel Texture unsupervised image segmentation

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unsupervised remote sensing image segmentation based on a dual autoencoder

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JOURNAL OF APPLIED REMOTE SENSING 2019年第3期13卷

作者： Zhang, Ruonan Yu, Long Tian, Shengwei Lv, Yalong Xinjiang Univ Coll Software Urumqi Peoples R China Xinjiang Univ Network Ctr Urumqi Peoples R China Xinjiang Univ Coll Informat Sci & Engn Urumqi Peoples R China

Aimed at solving the limitation of the traditional unsupervised segmentation method of remote sensing images that rely more on features and prior knowledge, an unsupervised remote sensing image segmentation method combined with the atrous convolution autoencoder and the bidirectional long- and short-time memory autoencoder autoencoder (BiLSTM) network is proposed. The multispectral remote senses images using the dimensional reduction capacity of the autoencoder to obtain the transverse low-dimensional spatial features of the image through the atrous convolution autoencoder, and they use the BiLSTM autoencoder to learn the longitudinal low-dimensional spectral features of the multiband sequence of the remote sensing image. The batch normalization layer is added to optimize the whole network, and the visualization method is used to dynamically train and adjust the model. The data clustering of two kinds of low-dimensional features is obtained by the softmax algorithm, and unsupervised image segmentation of the multispectral data is finally realized by fusing the clustering results. In the experiment, the original data of Landsat8 remote sensing images were clustered and segmented directly. The results show that the accuracy is up to 83.25% through the clustering of dual autoencoder network in the water information extraction, which is obviously better than the common clustering algorithm and can get better image segmentation results. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： remote-sensing image auto-encoder network unsupervised image segmentation clustering atrous convolution BiLSTM

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unsupervised fuzzy clustering and image segmentation using weighted neural networks

Unsupervised fuzzy clustering and image segmentation using w...

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12th International Conference on image Analysis and Processing

作者： Muhammed, HH Univ Uppsala Ctr Image Anal SE-75237 Uppsala Sweden

ISBN: (纸本)0769519482

A new class of neuro-fuzzy systems, based on so-called Weighted Neural Networks (WNN), is introduced and used for unsupervised fuzzy clustering and image segmentation. Incremental and fixed (or grid-partitioned) Weighted Neural Networks are presented and used for this purpose. The WNN algorithm (incremental or grid-partitioned) produces a net, of nodes connected by edges, which reflects and preserves the topology of the input data set. Additional weights, which are proportional to the local densities in the input space, are associated with the resulting nodes and edges to store useful information about the topological relations in the given input data set. A fuzziness factor, proportional to the connectedness of the net, is introduced in the system. A watershed-like procedure is used to cluster the resulting net. The number of resulting clusters is determined by this procedure. Experiments confirm the usefulness and efficiency of the proposed neuro-fuzzy systems for image segmentation and, in general, for clustering multi- and high-dimensional data.

关键词： unsupervised image segmentation unsupervised fuzzy clustering Neuro-Fuzzy systems Weighted Neural Networks (WNN) watersheds

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Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation

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JOURNAL OF VISUAL COMMUNICATION AND image REPRESENTATION 2016年 34卷 12-27页

作者： Zhu, Hongyuan Meng, Fanman Cai, Jianfei Lu, Shijian ASTAR Inst Infocomm Res Singapore Singapore Univ Elect Sci & Technol China Sch Elect Engn Chengdu Peoples R China Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

image segmentation refers to the process to divide an image into meaningful non-overlapping regions according to human perception, which has become a classic topic since the early ages of computer vision. A lot of research has been conducted and has resulted in many applications. While many segmentation algorithms exist, there are only a few sparse and outdated summarizations available. Thus, in this paper, we aim to provide a comprehensive review of the recent progress in the field. Covering 190 publications, we give an overview of broad segmentation topics including not only the classic unsupervised methods, but also the recent weakly-/semi-supervised methods and the fully-supervised methods. In addition, we review the existing influential datasets and evaluation metrics. We also suggest some design choices and research directions for future research in image segmentation. (C) 2015 Elsevier Inc. All rights reserved.

关键词： image segmentation Superpixel Interactive image segmentation Object proposal Semantic image parsing image cosegmentation unsupervised image segmentation Weakly-supervised image segmentation

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An Approach Toward Fast Gradient-Based image segmentation

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IEEE TRANSACTIONS ON image PROCESSING 2015年第9期24卷 2633-2645页

作者： Hell, Benjamin Kassubeck, Marc Bauszat, Pablo Eisemann, Martin Magnor, Marcus Tech Univ Carolo Wilhelmina Braunschweig Comp Graph Lab D-38106 Braunschweig Germany

In this paper, we present and investigate an approach to fast multilabel color image segmentation using convex optimization techniques. The presented model is in some ways related to the well-known Mumford-Shah model, but deviates in certain important aspects. The optimization problem has been designed with two goals in mind. The objective function should represent fundamental concepts of image segmentation, such as incorporation of weighted curve length and variation of intensity in the segmented regions, while allowing transformation into a convex concave saddle point problem that is computationally inexpensive to solve. This paper introduces such a model, the nontrivial transformation of this model into a convex-concave saddle point problem, and the numerical treatment of the problem. We evaluate our approach by applying our algorithm to various images and show that our results are competitive in terms of quality at unprecedentedly low computation times. Our algorithm allows high-quality segmentation of megapixel images in a few seconds and achieves interactive performance for low resolution images (Fig. 1).

关键词： unsupervised image segmentation convex optimization

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