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color image segmentation by Utilizing Coarse-to-Fine Strategy

JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2022年第7期31卷 2250123-2250123页

作者： Chen, Yingxia Chen, Yan Yi, Shanji Cao, Kun Yangtze Univ Sch Comp Sci Jingzhou 434023 Hubei Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Guangdong Peoples R China

image segmentation is an important processing technology, which is the basis of image recognition and has been widely used in many fields. In this paper, we propose a method, termed coarse-to-fine strategy-based image segmentation (CSIS), for color image segmentation. The basic idea is to segment an image by three phases: (1) the original image is first segmented into several distinct regions by using the mean shift method;(2) the segmented regions are converted to a weighted region adjacency graph (RAG), and a new graph cut method, called multi-cut algorithm, is proposed to partition the RAG into multiple regions;(3) a one-step Chan-Vese algorithm is applied to smooth the boundaries of the segmented objectives. In each of the last two phases, a method is applied to refine the result obtained in the previous phase. By carefully balancing the efforts used in each phase, CSIS could segment color images both efficiently and effectively. These advantages are demonstrated by applying the proposed method to a variety of test instances, and the statistical results also show that it is comparable with some state-of-the-art methods.

关键词： color image segmentation mean shift multi-cut Chan-Vese model coarse-to-fine strategy

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Multiobjective fuzzy clustering with multiple spatial information for Noisy color image segmentation

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APPLIED INTELLIGENCE 2021年第8期51卷 5280-5298页

作者： Liu, Hanqiang Zhao, Feng Shaanxi Normal Univ Minist Educ Key Lab Modern Teaching Technol 620 West Changan Ave Xian Peoples R China Shaanxi Normal Univ Sch Comp Sci 620 West Changan Ave Xian Peoples R China Xian Univ Posts & Telecommun Sch Telecommun & Informat Engn Xian Peoples R China

Clustering method is a widely used and effective technique in color image segmentation. In general, traditional clustering-based image segmentation algorithms consider only one objective function and the segmentation performance is easily influenced by the noise in the image. Therefore, utilizing many clustering criteria and the neighborhood statistic information of pixels are more effective to improve the segmentation performance. In this paper, we put forward a multiobjective fuzzy clustering algorithm with multiple spatial information (MFCMSI) for noisy color image segmentation. Firstly, two conflicting fitness functions including the local spatial information with a circular neighborhood window and the non-local spatial information with circular search and similarity windows are designed to improve the noise-resistant ability and segmentation performance. In order to optimize these two fitness functions, the variable-length and cluster-center-based encoding strategy and some efficient evolutionary operations are utilized in each generation. Finally, a cluster validity index with multiple spatial information is constructed to select the best solution from the final non-dominated solution set of the last generation. Aiming to improve the robustness of MFCMSI, the ensemble strategy is introduced into MFCMSI and an ensemble version of MFCMSI is presented. Experimental results show that MFCMSI and its ensemble version behave well in evolving the number of segments and obtaining the satisfactory segmentation performance.

关键词： color image segmentation Multiobjective optimization Fuzzy clustering Multiple spatial information Ensemble clustering

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color image segmentation using saturated RGB colors and decoupling the intensity from the hue

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第1-2期79卷 1555-1584页

作者： Garcia-Lamont, Farid Cervantes, Jair Lopez-Chau, Asdrubal Ruiz-Castilla, Sergio Univ Autonoma Estado Mexico Ctr Univ UAEM Texcoco Av Jardin Zumpango S-N Texcoco 56259 Estado De Mexic Mexico Univ Autonoma Estado Mexico Ctr Univ UAEM Zumpango Camino Viejo Jilotzingo Continuac Calle Rayon Zumpango 55600 Estado De Mexic Mexico

Although the RGB space is accepted to represent colors, it is not adequate for color processing. In related works the colors are usually mapped to other color spaces more suitable for color processing, but it may imply an important computational load because of the non-linear operations involved to map the colors between spaces;nevertheless, it is common to find in the state-of-the-art works using the RGB space. In this paper we introduce an approach for color image segmentation, using the RGB space to represent and process colors;where the chromaticity and the intensity are processed separately, mimicking the human perception of color, reducing the underlying sensitiveness to intensity of the RGB space. We show the hue of colors can be processed by training a self-organizing map with chromaticity samples of the most saturated colors, where the training set is small but very representative;once the neural network is trained it can be employed to process any given image without training it again. We create an intensity channel by extracting the magnitudes of the color vectors;by using the Otsu method, we compute the threshold values to divide the intensity range in three classes. We perform experiments with the Berkeley segmentation database;in order to show the benefits of our proposal, we perform experiments with a neural network trained with different colors by subsampling the RGB space, where the chromaticity and the intensity are processed jointly. We evaluate and compare quantitatively the segmented images obtained with both approaches. We claim to obtain competitive results with respect to related works.

关键词： RGB space color image segmentation Self-organizing maps Otsu method

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color image segmentation using proximal classifier and quaternion radial harmonic Fourier moments

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PATTERN ANALYSIS AND APPLICATIONS 2020年第2期23卷 683-702页

作者： Wang, Xiang-Yang Wang, Qian Wang, Xue-Bin Yang, Hong-Ying Wu, Zhi-Fang Niu, Pan-Pan Liaoning Normal Univ Sch Comp & Informat Technol Dalian 116029 Peoples R China Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Key Lab Media Secur Shenzhen 518060 Peoples R China

segmentation involves separating an object from the background in a given image. image segmentation has a variety of applications and has received considerable attention in multimedia application and computer vision. Although numerous approaches have been introduced, image segmentation is still far from being solved due to most of image segmentation algorithms are often so complicated and some unsatisfactory results appear frequently. Therefore, developing a suitable technique of image segmentation is still a challenging problem. In this article, a novel color image segmentation will be introduced based on quaternion radial harmonic Fourier moments (QRHFMs) and proximal classifier. Firstly, the image feature of pixel-level is represented by the accurate and invariant QRHFMs holistically as a vector field, which can describe sufficiently the image pixel information due to take into account the relationship among different color channels. Secondly, the image feature from pixel-level is utilized as the input of the proximal classifier with consistency (PCC), which not only has lower computation time but also has better generalization compared to traditional support vector machines classifiers. Then, we choose the training samples by Tsallis entropy thresholding to train PCC classification model. Finally, the color image is classified by the trained PCC classification model. Our algorithm can make full use of the accurate and robust local image feature, as well the quickness and generalization ability of PCC classifier. A series of experimental results shows that this algorithm has better segmentation performance than the state-of-the-art method from the literature.

关键词： color image segmentation Proximal classifier with consistency Quaternion radial harmonic Fourier moments Twin support vector machines Tsallis entropy

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A reliable region information driven kriging-assisted multiobjective rough fuzzy clustering algorithm for color image segmentation

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期228卷

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

Multiobjective clustering algorithms (MOCAs) are becoming increasingly popular with the merit of segmenting images from multiple perspectives. The performances of MOCAs highly depend on their fitness functions. However, most existing MOCAs adopt one pair of complementary fitness functions, which always measure intraclass compactness and inter-class separation, respectively. This may result in insufficient ability to recognize and mine feature structures from complex images. Moreover, information within color images, such as region information and uncertain information, can barely receive enough attention in MOCAs. To resolve these problems, we propose a reliable region information driven Kriging-assisted multiobjective rough fuzzy clustering algorithm (RRI-KMRFC). Firstly, a reliability-based region information extraction strategy (RRIES) is designed to obtain reliable image information with satisfactory regional homogeneity and abundant image details. Secondly, the derived region information is used to construct three complementary fitness functions, focusing on rough intraclass compactness, rough inter-class separation, and regional consistency, respectively. Such fitness functions can effectively identify the clustering structure, maintain contour details, and characterize uncertain information from color images. To efficiently optimize the proposed functions, an incremental Kriging-assisted evolutionary framework is presented to decrease the expensive function evaluations in which an improved infill sampling strategy is devised to assist in finding unexplored areas in the decision space. Finally, a rough fuzzy clustering validity index with reliable region information is proposed to select the optimal trade-off solution. Experiments performed on Berkeley and Weizmann images confirm the effectiveness and robustness of RRI-KMRFC.

关键词： color image segmentation Kriging surrogate Multiobjective optimization Region information extraction Rough fuzzy clustering

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The Algorithm of Watershed color image segmentation Based on Morphological Gradient

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SENSORS 2022年第21期22卷 8202-8202页

作者： Wu, Yanyan Li, Qian Ningbo Univ Finance & Econ Coll Digital Technol & Engn Ningbo 315175 Peoples R China

The traditional watershed algorithm has the disadvantage of over-segmentation and interference with an image by reflected light. We propose an improved watershed color image segmentation algorithm. It is based on a morphological gradient. This method obtains the component gradient of a color image in a new color space is not disturbed by the reflected light. The gradient image is reconstructed by opening and closing. Therefore, the final gradient image is obtained. The maximum inter-class variance algorithm is used to obtain the threshold automatically for the final gradient image. The original gradient image is forcibly calibrated with the obtained binary labeled image, and the modified gradient image is segmented by watershed. Experimental results show that the proposed method can obtain an accurate and continuous target contour. It will achieve the minimum number of segmentation regions following human vision. Compared with similar algorithms, this way can suppress the meaningless area generated by the reflected light. It will maintain the edge information of the object well. It will improve the robustness and applicability. From the experimental results, it can be seen that compared with the region-growing method and the automatic threshold method;the proposed algorithm has a great improvement in operation efficiency, which increased by 10%. The accuracy and recall rate of the proposed algorithm is more than 0.98. Through the experimental comparison, the advantages of the proposed algorithm in object segmentation can be more intuitively illustrated.

关键词： color image segmentation multistage gradient edge detection watershed algorithm

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HWOA: A hybrid whale optimization algorithm with a novel local minima avoidance method for multi-level thresholding color image segmentation

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期190卷 116145-116145页

作者： Abdel-Basset, Mohamed Mohamed, Reda AbdelAziz, Nabil M. Abouhawwash, Mohamed Zagazig Univ Zagazig 44519 Ash Sharqia Gov Egypt Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt Michigan State Univ Dept Computat Math Sci & Engn CMSE E Lansing MI 48824 USA

Traditional methods to address color image segmentation work efficiently for bi-level thresholding. However, for multi-level thresholding, traditional methods suffer from time complexity that increases exponentially with the increasing number of threshold levels. To overcome this problem, in this paper, a new approach is proposed to tackle multi-threshold color image segmentation by employing the Otsu method as an objective function. This approach is based on a hybrid of the whale optimization algorithm (WOA) with a novel method called the local minima avoidance method (LMAM), abbreviated as HWOA. LMAM avoids local minima by updating the whale either within the search space of the problem or between two whales selected randomly from the population-based on a certain probability. HWOA is validated on ten color images taken from the Berkeley University Dataset by measuring the objective values, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), features similarity index (FSIM), and CPU time, and compared with a number of the well-known robust meta-heuristic algorithms: the sine-cosine algorithm (SCA), WOA, modified salp swarm algorithm (MSSA), improved marine predators algorithm (IMPA), modified Cuckoo Search (CS) using McCulloch's algorithm (CSMC), and equilibrium optimizer (EO). The experimental results show that HWOA is superior to all the other algorithms in terms of PSNR, FSIM, and objective values, and is competitive in terms of SSIM.

关键词： color image segmentation Whale optimization algorithm Otsu method Local minima elimination method Multi-level thresholding

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color image segmentation Based on NCut 20

Color Image Segmentation Based on NCut

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Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering

作者： Fu Shuang Yang Dun-jiong Department of Information Science and Technology Xiamen University Tan Kahkee College Zhang Zhou Fu Jian China

ISBN: (纸本)9781450387811

color image segmentation using traditional Normalized Cut method with a Gaussian kernel function is sensitive to the selection of its parameters. In order to do it without any parameter, this paper treats color image pixels as a three-dimensional vector in a RGB space, use the difference in direction and amplitude of vectors to measure the similarity of pixels, then segment the image with the Normalized Cut method. Experiments showed that this approach can differentiate the color information effectively, and get good results in color image segmentation.

关键词： graph theory weight function color image segmentation normalized cut

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A color image segmentation Method Based on Region Salient color and Fuzzy C-Means Algorithm

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2020年第2期39卷 586-610页

作者： Feng, Lei Li, Haibin Gao, Yakun Zhang, Yakun Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Xingtai Polytech Coll Xingtai 054035 Peoples R China

This paper proposes a color image segmentation method based on region salient color and the fuzzy C-means (FCM) algorithm. The method first uses the convex hull theory based on Harris corner detection to detect the object of the image. Thus, the object and the background can be separated. Then, the quantized color histogram can be studied in the HSV color space. By calculating the number of the peak values of both the object and the background histograms, the quantity of the regional salient colors can be obtained. The quantity is the number of the clustering centroids of FCM algorithm. Finally, the FCM algorithm and the noise correction algorithm can be used in the object and the background, respectively. The obtained segmented image consists of the object and the background segmentation. It proves that the method in this paper is an effective segmentation method based on the experiments made by use of Berkeley segmentation dataset. According to the experimental results, it can be concluded that the proposed algorithm has the highest segmentation accuracy and the shortest computing time among the algorithms mentioned in this paper. The algorithm can achieve high-quality, stable and accurate color image segmentation results.

关键词： color image segmentation Region salient color Fuzzy C-means color quantization color histogram

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Efficient color image segmentation via Quaternion-based L1/L2 Regularization

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JOURNAL OF SCIENTIFIC COMPUTING 2022年第1期93卷 9-9页

作者： Wu, Tingting Mao, Zhihui Li, Zeyu Zeng, Yonghua Zeng, Tieyong Nanjing Univ Posts & Telecommun Sch Sci Nanjing 210023 Peoples R China Chinese Univ Hong Kong Dept Math Shatin Hong Kong Peoples R China Army Engn Univ PLA Coll Field Engn Nanjing 210007 Peoples R China

color image segmentation is a key technology in image processing. In this paper, a two-stage image segmentation method is proposed that is based on the nonconvex L-1/L-2 approximation of the Mumford-Shah (MS) model. Wherein, the nonconvex regularization term L-1/L-2 on the gradient can approximate the Hausdorff measure and extract more boundary information. The first stage is to solve the nonconvex variant of the MS model and to obtain the smoothed image u. In our framework, we utilize the semi-proximal alternating direction method of multipliers (sPADMM) to sufficiently solve the proposed model. The second stage is segmenting the smoothed u into different phases with thresholds determined by the threshold clustering method. To better deal with the inherent color structures within different channels, we also apply the quaternion representation of the color image. Quantitative and qualitative results demonstrate clearly that our method is better than some state-of-the-art color image segmentation methods.

关键词： color image segmentation Quaternion L-1/L-2 regularization sPADMM Convergence analysis

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