Color Image Segmentation Research Articles

Segmentation strategies for the alpha-tree data structure

The alpha-tree is a versatile algorithm for color image segmentation employing attribute constraints to control the partitioning of the alpha-connected image space. Attribute constraints are enforced using a maximization strategy that returns the set of the largest connected components complying with these constraints assuming no prior violations from nested sub-components. This article presents two new strategies extending the way this set is defined. These are the non-target clustering and attribute maximization strategies, that give access to segments that could not be defined previously. Collectively they enable the handling of texture-rich regions that cannot be clustered into meaningful segments, and compute the unsupervised segmentation of images by seeking for extreme attribute values.

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

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.

Intuitionistic fuzzy set and fuzzy mathematical morphology applied to color leukocytes segmentation

This work presents a new algorithm based on Atanassov’s intuitionistic fuzzy sets and fuzzy mathematical morphology to leukocytes segmentation in color images. The main idea is based on modeling a color image as an Atanassov’s intuitionistic fuzzy set using the hue component in the HSV color space. Then, a pixel labeled as leukocyte is selected and compared to the whole image with a similarity measure. Thus, the leukocyte is segmented and separated from the rest of the image. The experimental results show that the algorithm has a good performance, reaching a value of 99.41% for the correct classification of leukocytes and a 99.23% for the correct classification of the background. Other metrics such as accuracy, precision and recall have been calculated obtaining 99.32%, 99.41% and 99.24%, respectively. The algorithm presents two important characteristics: It works directly over the color images without the need of converting the image in gray scale, and it does not produce false colors because fuzzy morphological operators guarantee it.

Modified salp swarm algorithm based multilevel thresholding for color image segmentation.

This paper proposes a multi-threshold image segmentation method based on modified salp swarm algorithm (SSA). Multi-threshold image segmentation method has good segmentation effect, but the segmentation precision will be affected with the increase of threshold number. To avoid the above problem, the slap swarm optimization algorithm (SSA) is presented to choose the optimal parameters of the fitting function and we use levy flight to improve the SSA. The solutions are assessed using the Kapur's entropy, Otsu and Renyi entropy fitness function during the optimization operation. The performance of the proposed algorithm is evaluated with several reference images and compared with different group algorithms. The results have been analyzed based on the best fitness values, peak signal to noise ratio (PSNR), and feature similarity index measures (FSIM). The experimental results show that the proposed algorithm outperformed other swarm algorithms.

Modified thermal exchange optimization based multilevel thresholding for color image segmentation

This paper proposes a multi-threshold image segmentation method based on modified thermal exchange optimization (TEO). Although it is efficient and gives excellent result in the case of bi-level thresholding, but it takes a lot of computation when the number of threshold increases. To overcome this problem, the TEO algorithm is applied in this search area for searching the optimal thresholds. The Levy flight algorithm is employed to modify the original TEO and balance the exploration and exploitation of TEO. Experiments are conducted between six state-of-the-art metaheuristic algorithms and the proposed one. The benchmark functions, color nature images and stellite images are used in the experiment to test the performance of the algorithms involved. Qualitative experimental results show that the proposed segmentation approach has fewer iterations and higher segmentation accuracy.

GPU Accelerated Fuzzy C-Means (FCM) Color Image Segmentation

In this paper, computational acceleration of color image segmentation using fuzzy c-means (FCM) algorithm has been presented. The color image is first converted from the Red Green Blue (RGB) color space to the YUV color space. Then, the luma (Y) information values are grouped according to the desired number of clusters using the FCM algorithm. The FCM algorithm is implemented on a Graphical Processing Unit (GPU) using the Compute Unified Device Library (CUDA) library which is developed by NVidia to speed up the computing time. Images used in this research are red blood cell images, geometry images and leaf images. The results of segmented images processed using GPU were seen identic to the results of segmented images processed using the Central Processing Unit (CPU). The computational time of the FCM algorithm can be accelerated by speed-up to 5,628 times faster and the average speed-up of all simulations done is 5,517 times faster.

An efficient optimal multilevel image thresholding with electromagnetism-like mechanism

Segmentation process is considered a major part of various image-processing applications due to its extreme inspiration on the subsequent image analysis. Thresholding is one of the simplest techniques for segmentation. In this paper, Renyi’s entropy is combined with electromagnetism-like mechanism optimization (EMO) to perform multilevel thresholding based color image segmentation. For statistical independent subsystems, Renyi’s entropy shows an extensive property and is applied to find best threshold value for image segmentation. The entropic parameter α can handle the additive information that is existent in the image. The feasibility of the EMO-Renyi’s based approach has been tested on various satellite and standard color images with bat algorithm (BAT), backtracking search algorithm (BSA), firefly algorithm (FA), particle swarm optimization (PSO), and wind driven optimization (WDO) for solving the multilevel color image thresholding problem. The analysis based on statistics of different optimization algorithms indicates the proposed EMO-Renyi’s algorithm to be more robust and precise for multilevel color image segmentation problem. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy. Experiments on standard daily-life color images are conducted to prove the effectiveness of the proposed scheme. The results show that the proposed method can produce more promising segmentation results from the aspect of objective and subjective observations.

Segmentation of colour image using fuzzy sets concept

The article deals with the problem of segmentation of digital images, which is one of the main tasks in the field of digital image processing (IP) and computer vision. To solve this problem, an algorithm was proposed based on the use of a concept based on the theory of fuzzy sets. The main idea of the proposed algorithm is the formation of subsets of interconnected pixels based on the fuzzy-to-mean method. A distinctive feature of the proposed algorithm is the definition of a set of features that define areas with similar characteristics in the space of the characteristic features of the analyzed image. The proposed segmentation algorithm (SA) consists of two stages: 1) the formation of characteristic features for all channels of the base color; 2) clustering of image elements. The practical significance of the obtained results lies in the fact that the developed models of algorithms can be used in various applied problems, where the classification of objects represented as images is provided. To test the efficiency of the developed algorithm, experimental studies were carried out in solving a number of applied problems related to color image segmentation, in particular, license plate recognition problems.

Double Line Clustering based Colour Image Segmentation Technique for Plant Disease Detection.

Agriculture is one of the most essential industry that fullfills people's need and also plays an important role in economic evolution of the nation. However, there is a gap between the agriculture sector and the technological industry and the agriculture plants are mostly affected by diseases, such as the bacterial, fungus and viral diseases that lead to loss in crop yield. The affected parts of the plants need to be identified at the beginning stage to eliminate the huge loss in productivity. In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. The efficiency of the system is implemented in tomato, grape, cucumber plants leaf images and the results are analyzed in terms of the error rate, sensitivity, specificity, accuracy and time. The result of the clustering algorithm achieved high accuracy, sensitivity, and specificity. The feature extraction is applied after the clustering process which produces minimum error rate.

An Integrated Color Image Segmentation with Multi-class SVM followed by SRFCM

In existing the segmentation of a color image is mostly depends on the features color , texture or on both color and texture but proposed method for color image segmentation is based on both color and texture with multi-class SVM (Support Vector Machine).For color feature extraction we used homogeneity model and for textural features we used PLD (Power Law Descriptor). With the help of SR-FCM (Soft Rough Fuzzy-C-Means) clustering. Membership functions based on the fuzzy set are facing the major problem of cluster overlapping. The rough set concepts can help us to get correct data from incomplete data, uncertainty of data. For defining the soft set theory there is no any requirement of parameterization tools. To get improved results of proposed algorithm the combination of aspects of fuzzy sets, rough sets as well as soft sets are used. The feature extraction for textural feature is done by using spatial domain which helps to reduce the run time complexity. Proposed method provides better performance which is compared with all the state of art techniques which is developed and analyzed using MATLAB.

Automatic Segmentation of Images with Superpixel Similarity Combined with Deep Learning

In this paper, we combine superpixel and deep learning models to propose a new unsupervised image segmentation based on region-combined color images. Compared to other region merging algorithms, our algorithm can automatically segment color images without human interaction. The algorithm has three phases. In the first phase, we use the mean shift algorithm to obtain non-overlapping over-segmented regions. Firstly, the image is initially segmented by the superpixel segmentation algorithm, then the saliency map is obtained by the superpixel similarity, and the semi-supervised region is merged into an unsupervised algorithm by the saliency map. Finally, the resulting picture is sent to the deep learning model for training to get the final segmentation picture. A large number of experiments have been carried out, and the results show that the scheme can reliably extract the contour of the object from the complex background.

Reducing overlapped pixels: a multi-objective color thresholding approach

This paper proposes a general multi-objective thresholding segmentation methodology for color images and a quality metric designed to prevent and quantify the overlapping effect of segmented images. Multi-level thresholding (MTH) has been used to segment color images in recent years; this process considers each channel as a single grayscale image and applies the MTH independently. Although this method provides competitive results, the inherent relationship among color channels is disregarded. Such approaches generate spurious classes on overlapping regions, where new colors are generated, especially on the borders of the objects. The proposed multi-objective color thresholding (MOCTH) approach performs image segmentation while preserving the relationship between image channels. MOCTH is aimed to reduce the overlapping effect on segmented color images without performing additional post-processing. To measure the overlapping classes on a thresholded color image, the overlapping index is proposed to quantify the pixels affected. The presented approach is analyzed on two color spaces (RGB and CIE L*a*b*) using three multi-objective algorithms; they are NSGA-III, SPEA-2, and MOPSO. Results provide evidence pointing out to a better segmentation from MOCTH over the traditional single-objective approaches while reducing overlapped areas on the image.

Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation

A great number of improved fuzzy c-means (FCM) clustering algorithms have been widely used for grayscale and color image segmentation. However, most of them are time-consuming and unable to provide desired segmentation results for color images due to two reasons. The first one is that the incorporation of local spatial information often causes a high computational complexity due to the repeated distance computation between clustering centers and pixels within a local neighboring window. The other one is that a regular neighboring window usually breaks up the real local spatial structure of images and thus leads to a poor segmentation. In this work, we propose a superpixel-based fast FCM clustering algorithm that is significantly faster and more robust than state-of-the-art clustering algorithms for color image segmentation. To obtain better local spatial neighborhoods, we first define a multiscale morphological gradient reconstruction operation to obtain a superpixel image with accurate contour. In contrast to traditional neighboring window of fixed size and shape, the superpixel image provides better adaptive and irregular local spatial neighborhoods that are helpful for improving color image segmentation. Second, based on the obtained superpixel image, the original color image is simplified efficiently and its histogram is computed easily by counting the number of pixels in each region of the superpixel image. Finally, we implement FCM with histogram parameter on the superpixel image to obtain the final segmentation result. Experiments performed on synthetic images and real images demonstrate that the proposed algorithm provides better segmentation results and takes less time than state-of-the-art clustering algorithms for color image segmentation.

Segmentation algorithm for Hangzhou white chrysanthemums based on least squares support vector machine

In order to realize the visual positioning for Hangzhou white chrysanthemums harvesting robot in natural environment, a color image segmentation method for Hangzhou white chrysanthemum based on least squares support vector machine (LS-SVM) was proposed. Firstly, bilateral filter was used to filter the RGB channels image respectively to eliminate noise. Then the pixel-level color feature and texture feature of the image, which was used as input of LS-SVM model (classifier) and SVM model (classifier), were extracted via RGB value of image and gray level co-occurrence matrix. Finally, the color image was segmented with the trained LS-SVM model (classifier) and SVM model (classifier) separately. The experimental results showed that the trained LS-SVM model and SVM model could effectively segment the images of the Hangzhou white chrysanthemums from complicated background taken under three illumination conditions such as front-lighting, back-lighting and overshadow, with the accuracy of above 90%. When segmenting an image, the SVM algorithm required 1.3 s, while the LS-SVM algorithm proposed in this paper just needed 0.7 s, which was better than the SVM algorithm obviously. The picking experiment was carried out and the results showed that the implementation of the proposed segmentation algorithm on the picking robot could achieve 81% picking success rate. Keywords: bilateral filter, least squares support vector machine (LS-SVM), image segmentation, Hangzhou white chrysanthemum, illumination intensity DOI: 10.25165/j.ijabe.20191204.4584 Citation: Yang Q H, Luo S L, Chang C, Xun Y, Bao G J. Segmentation algorithm for Hangzhou white chrysanthemums based on least squares support vector machine. Int J Agric & Biol Eng, 2019; 12(4): 127–134.

Review Paper on Segmentation of Color Image using Morphological Processing

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Automatic Segmentation of Natural Color Images in CIE Lab Space using Possibilistic Fuzzy C Means Clustering

Clustering is the most significant assignment in image processing. This work performs the segmentation of natural color images in CIELab space based on the Possibilistic fuzzy c means clustering (PFCM).The basic principle of the proposed approach is the segmentation of natural color images based on the two-way approach of hill climbing (HC) and PFCM. In this work, RGB image is transformed into CIELab space for the efficient extraction of the secreted treasure in the images. The combined approach of local optimization search technique, HC and PFCM is applied for the segmentation of synthetic fiber images. This color histogram based technique works on the principle of identification of peaks in the color histogram of the natural color image. The identified peaks are considered as initial seed or clusters. These seeds are then applied to the PFCM to perform the final segmentation. Investigational outcomedemonstrates the competence of thetwo-way approach of HC and PFCMwhich presents the preeminentend result for less complexity color images.

Detecting salient objects in non-stationary video image sequence for analyzing user perceptions of digital video contents

The ubiquitous utilization of video applications in recent years has made research on video quality of experience paramount. Lack of sufficient bandwidth deters the effective transmission of raw video contents to users. This bandwidth challenge has given rise to encoders for compressing digital video contents for transmission over an internet protocol infrastructure. However, transmitting compressed video color images still has an intrinsic limitation of high bandwidth consumption. Simple linear iterative clustering algorithm was applied for binary segmentation of video color images to circumvent the challenge of efficiently transmitting video contents. Compressed binary segmented images are generally fast to transmit and require lower bandwidth consumption as opposed to compressed video color images. However, since color images contain more useful information than binary image counterparts, evaluation of binary segmentation results was performed using the mean opinion score metric to determine user quality of experience of the transmitted video contents. The practical application of our method will lead to the development of a novel encoder that can deliver binary video contents faster, hence solving the bandwidth hiccup.

Efficient clustering approach for adaptive unsupervised colour image segmentation

This study proposes a clustering-based colour image segmentation approach consisting of a novel initialisation technique. Colour image segmentation transforms image pixels into regions and a prerequisite for image analysis and computer vision applications. Therefore, colour image segmentation is considered one of the most important processes in image understanding and pattern recognition. This study presents an efficient and adaptive unsupervised approach based on bottom-up red–green–blue (RGB) colour histogram search approach to achieve colour image segmentation. Firstly, the RGB histogram is processed through a double-scan procedure to determine significant modes in each histogram. In the next step, each mode is processed through a bottom-up histogram search approach, completing RGB triplet. The RGB triplets are utilised as the cluster centroids, clustering the pixels into regions and producing the final segmented image. The authors proposed method was compared with several other unsupervised image segmentation algorithms with an extensive experiment performed on various image segmentation evaluation benchmarks. Experimental results show that the proposed algorithm outperforms state-of-the-art algorithms both in terms of features integrity and execution speed.

3DPCNN based on whale optimization algorithm for color image segmentation

3DPCNN based on whale optimization algorithm for color image segmentation

Cuttlefish Algorithm-Based Multilevel 3-D Otsu Function for Color Image Segmentation

To overcome the shortcomings of 1-D and 2-D Otsu's thresholding methods, a 3-D Otsu method has been introduced. While yielding satisfactory segmentation results for images with a low signal-to-noise ratio (SNR) and poor contrast, it has the downside of high computational complexity. In this paper, the cuttlefish algorithm (CFA)-based 3-D Otsu thresholding method is proposed to pace up the conventional 3-D Otsu thresholding for color image segmentation. In order to decrease the effects of noises and weak edges, an optimally selected multilevel 3-D Otsu image thresholding technique is brought into the proposed segmentation scheme. The CFA is a newly developed stochastic meta-heuristic optimization algorithm based on observing, mimicking, and modeling the camouflaging feature of cuttlefish. It is used to simplify the problem of exhaustive search for the optimal threshold vector in 3-D space. Experimental results, when compared to 1-D Otsu, 1-D Otsu-Cuckoo search (CS) algorithm, 1-D Otsu-lightning search algorithm (LSA), 1-D Otsu-CFA, conventional 3-D Otsu, 3-D Otsu-CS, and 3-D Otsu-LSA, indicate that the proposed algorithm CFA-based 3-D Otsu thresholding is superior to all the other multilevel thresholding algorithms. The proposed 3-D-CFA method produces promising segmentation results from the objective and subjective aspects.