Unsupervised Color Image Segmentation Research Articles

Nonparametric K-means clustering-based adaptive unsupervised colour image segmentation

Nonparametric K-means clustering-based adaptive unsupervised colour image segmentation

Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization.

Color-based image segmentation classifies pixels of digital images in numerous groups for further analysis in computer vision, pattern recognition, image understanding, and image processing applications. Various algorithms have been developed for image segmentation, but clustering algorithms play an important role in the segmentation of digital images. This paper presents a novel and adaptive initialization approach to determine the number of clusters and find the initial central points of clusters for the standard K-means algorithm to solve the segmentation problem of color images. The presented scheme uses a scanning procedure of the paired Red, Green, and Blue (RGB) color-channel histograms for determining the most salient modes in every histogram. Next, the histogram thresholding is applied and a search in every histogram mode is performed to accomplish RGB pairs. These RGB pairs are used as the initial cluster centers and cluster numbers that clustered each pixel into the appropriate region for generating the homogeneous regions. The proposed technique determines the best initialization parameters for the conventional K-means clustering technique. In this paper, the proposed approach was compared with various unsupervised image segmentation techniques on various image segmentation benchmarks. Furthermore, we made use of a ranking approach inspired by the Evaluation Based on Distance from Average Solution (EDAS) method to account for segmentation integrity. The experimental results show that the proposed technique outperforms the other existing clustering techniques by optimizing the segmentation quality and possibly reducing the classification error.

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.

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

Abstract 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.

A competition in unsupervised color image segmentation

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.

Unsupervised segmentation of low-contrast multichannel images: discrimination of tissue components in microscopic images of unstained specimens.

Low-contrast images, such as color microscopic images of unstained histological specimens, are composed of objects with highly correlated spectral profiles. Such images are very hard to segment. Here, we present a method that nonlinearly maps low-contrast color image into an image with an increased number of non-physical channels and a decreased correlation between spectral profiles. The method is a proof-of-concept validated on the unsupervised segmentation of color images of unstained specimens, in which case the tissue components appear colorless when viewed under the light microscope. Specimens of human hepatocellular carcinoma, human liver with metastasis from colon and gastric cancer and mouse fatty liver were used for validation. The average correlation between the spectral profiles of the tissue components was greater than 0.9985, and the worst case correlation was greater than 0.9997. The proposed method can potentially be applied to the segmentation of low-contrast multichannel images with high spatial resolution that arise in other imaging modalities.

Unsupervised color-image segmentation by multicolor space iterative pixel classification

We propose a color-image segmentation algorithm by unsupervised classification of pixels. The originality of the proposed approach consists in iteratively identifying pixel classes by taking into account both the pixel color distributions in several color spaces and their spatial arrangement in the image. In order to overcome the difficult problem of the color space choice, the algorithm selects the color space that is well suited to construct the class at each iteration step. The selection criterion is based on connectedness and color homogeneity measures of pixel subsets. In order to tune the sensitivity of segmentation, we introduce a hierarchical criterion that allows us to segment images with different numbers of regions as human observers do. Experiments carried out on the well-known Berkeley segmentation dataset show that this multicolor space approach succeeds in constructing classes that effectively correspond to regions in the image.

KmsGC: An Unsupervised Color Image Segmentation Algorithm Based onK-Means Clustering and Graph Cut

For unsupervised color image segmentation, we propose a two-stage algorithm, KmsGC, that combinesK-means clustering with graph cut. In the first stage,K-means clustering algorithm is applied to make an initial clustering, and the optimal number of clusters is automatically determined by a compactness criterion that is established to find clustering with maximum intercluster distance and minimum intracluster variance. In the second stage, a multiple terminal vertices weighted graph is constructed based on an energy function, and the image is segmented according to a minimum cost multiway cut. A large number of performance evaluations are carried out, and the experimental results indicate the proposed approach is effective compared to other existing image segmentation algorithms on the Berkeley image database.

Color image segmentation: a novel spatial fuzzy genetic algorithm

Image segmentation is a very important low-level vision task. It is the perceptual grouping of pixels based on some similarity criteria. In this article, we have applied spatial fuzzy genetic algorithm (SFGA) for the unsupervised segmentation of color images. The SFGA adds diversity to the search process to find the global optima. The performance of SFGA is influenced by two factors: first, K number of clusters—should be known in advance; second, the initialization of the cluster centers. To overcome these issues, a progressive technique based on self-organizing map is presented to find out the optimal K number of clusters automatically. To handle the initialization problem, peaks are identified using the image color histograms. The genetic algorithm with fuzzy behavior maximizes the fuzzy separation and minimizes the global compactness among the segments. The segmentation is performed on wavelet transform image which not only reduces the dimensionality and computational cost but also makes more compact segments. A novel pruning technique is proposed to handle the problem of over-segmentation. The results show that the proposed technique outperforms state-of-the-art methods.

Unsupervised colour image segmentation by low-level perceptual grouping

This paper proposes a new unsupervised approach for colour image segmentation. A hierarchy of image partitions is created on the basis of a function that merges spatially connected regions according to primary perceptual criteria. Likewise, a global function that measures the goodness of each defined partition is used to choose the best low-level perceptual grouping in the hierarchy. Contributions also include a comparative study with five unsupervised colour image segmentation techniques. These techniques have been frequently used as a reference in other comparisons. The results obtained by each method have been systematically evaluated using four well-known unsupervised measures for judging the segmentation quality. Our methodology has globally shown the best performance, obtaining better results in three out of four of these segmentation quality measures. Experiments will also show that our proposal finds low-level perceptual solutions that are highly correlated with the ones provided by humans.

Constrained Compound Markov Random Field Model with Graduated Penalty Function for Color Image Segmentation

In this paper, an unsupervised color image segmentation scheme has been proposed for preserving strong and weak edges as well. A Constrained Compound Markov Random Field (MRF) has been proposed as the a priori model for the color labels. We have used Ohta (I1, I2, I3) color model and a controlled correlation of the color space has been accomplished by the proposed compound MRF model. The Constrained Compound MRF (CCMRF) is found to possess the unifying property of modeling scenes as well as color textures. In unsupervised scheme, the associated model parameters and the image labels are estimated recursively. The model parameters are the Maximum Conditional Pseudo Likelihood (MCPL) estimates and the labels are the Maximum a Posteriori (MAP) estimates. The performance of the proposed scheme has been compared with that of Yu’s method and has been found to exhibit improved performance in the context of misclassification error.

多尺度无监督彩色图像分割

Nonsubsampled contourlet transform is a new multi-scale multi-resolution powerful analysis tool.An unsupervised segmentation algorithm for color image is proposed based on nonsubsampled contourlet transform.Firstly,for nonsubsampled contourlet transform shift invariance,multi-scale edge is extracted in transform domain by using gradient vector method.Then,local low-frequency energy texture features and high-frequency multi-scale Zernike moments texture features are extracted from low-frequency sub-band and high-frequency sub-band in transform domain and fusing them.Finally,detecting seed points in edge map to represent color image regions,the region growing followed by region merging method is applied for segmentation by color and texture Euclidean distance.The experimental results show that the algorithm can automatically fulfill unsupervised segmentation for color image by combining color,multi-scale edge and texture properly,and has more precise and more robust segmentation effect than traditional algorithm.

Unsupervised colour image segmentation using dual-tree complex wavelet transform

In this paper we present an effective unsupervised colour image segmentation algorithm which uses multiscale edge information and spatial colour content. The multiscale edge information is extracted using the dual-tree complex wavelet transform. Binary morphological operators are applied to the edge information to detect seed regions which are large enough to exclude boundary-only regions. The segmentation of homogeneous regions is obtained using region growing followed by region merging in the CIE L ∗ a ∗ b ∗ colour space. We also present an edge preserving smoothing filter as a pre-process for the algorithm. We compare our algorithm with state-of-the-art algorithms and show its superior performance.

Multiresolution adaptive and progressive gradient-based color-image segmentation

We propose a novel unsupervised multiresolution adaptive and progressive gradient-based color-image segmentation algorithm (MAPGSEG) that takes advantage of gradient information in an adaptive and progressive framework. The proposed methodology is initiated with a dyadic wavelet decomposition scheme of an arbitrary input image accompanied by a vector gradient calculation of its color-converted counterpart in the 1976 Commission Internationale de l'Eclairage (CIE) L*a*b* color space. The resultant gradient map is used to automatically and adaptively generate thresholds to segregate regions of varying gradient densities at different resolution levels of the input image pyramid. At each level, the classification obtained by a progressively thresholded growth procedure is integrated with an entropy-based texture model by using a unique region-merging procedure to obtain an interim segmentation. A confidence map and nonlinear spatial filtering techniques are combined, and regions of high confidence are passed from one resolution level to another until the final segmentation at the highest (original) resolution is achieved. A performance evaluation of our results on several hundred images with a recently proposed metric called the normalized probabilistic Rand index demonstrates that the proposed work computationally outperforms published segmentation techniques with superior quality.

Automatic Image Segmentation by Dynamic Region Growth and Multiresolution Merging

Image segmentation is a fundamental task in many computer vision applications. In this paper, we propose a new unsupervised color image segmentation algorithm, which exploits the information obtained from detecting edges in color images in the CIE L *a *b * color space. To this effect, by using a color gradient detection technique, pixels without edges are clustered and labeled individually to identify some initial portion of the input image content. Elements that contain higher gradient densities are included by the dynamic generation of clusters as the algorithm progresses. Texture modeling is performed by color quantization and local entropy computation of the quantized image. The obtained texture and color information along with a region growth map consisting of all fully grown regions are used to perform a unique multiresolution merging procedure to blend regions with similar characteristics. Experimental results obtained in comparison to published segmentation techniques demonstrate the performance advantages of the proposed method.

Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation

In this paper, we present a novel multiscale texture model and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled, in turn, by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model. The "fragmentation" step allows one to find the elementary textures of the model, while the "reconstruction" step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural and remote sensing images.

A customized Gabor filter for unsupervised color image segmentation

This paper presents work on accurate image segmentation utilizing local image characteristics. Image features are measured by employing an appropriate Gabor filter with adaptively chosen size, orientation, frequency and phase for each pixel. An image property called phase divergence is used for the selection of the appropriate filter size. Characteristic features related to the change in brightness, color, texture and position are extracted for each pixel at the selected size of the filter. In order to cluster the pixels into different regions, the joint distribution of these pixel features is modeled by a mixture of Gaussians utilizing three variants of the expectation maximization (EM) algorithm. The three different versions of EM used in this work for unsupervised clustering are: (1) penalized EM, (2) penalized stochastic EM, and (3) penalized inverse EM. Given the desired number of Gaussian mixture components, all three EM algorithms estimate the parameters of the mixture of Gaussians model that represents the joint distribution of pixel features. We determine the value of the number of models that best suits the natural number of clusters present in the image based on the Schwarz criterion, which maximizes the posterior probability of the number of groups given the samples of observation. This segmentation algorithm has been tested on the images of the Berkeley segmentation benchmark and the performance has demonstrated the effectiveness, accuracy and superiority of the proposed method.

A BYY scale-incremental EM algorithm for Gaussian mixture learning

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.

A fast fixed-point BYY harmony learning algorithm on Gaussian mixture with automated model selection

The Bayesian Ying–Yang (BYY) harmony learning theory has brought about a new mechanism that model selection on Gaussian mixture can be made automatically during parameter learning via maximization of a harmony function on finite mixture defined through a specific bidirectional architecture (BI-architecture) of the BYY learning system. In this paper, we propose a fast fixed-point learning algorithm for efficiently implementing maximization of the harmony function on Gaussian mixture with automated model selection. Several simulation experiments are performed to compare its effectiveness in automated model selection as well as its efficiency in parameter learning with other existing learning algorithms. The experimental results reveal that the performance of the proposed algorithm is superior to its counterparts in these aspects. Moreover, the proposed algorithm is further tested with three typical real-world data sets and successfully applied to unsupervised color image segmentation.

Texture and color segmentation based on the combined use of the structure tensor and the image components

In this paper, we propose a novel segmentation scheme for textured gray-level and color images based on the combined use of the local structure tensor and the original image components. The structure tensor is a well-established tool for image segmentation and has been successfully employed for unsupervised segmentation of textured gray-level and color images. The original image components can also provide very useful information. Therefore, a combined segmentation approach has been designed that combines both elements within a common energy minimization framework. Besides, an original method is proposed to dynamically adapt the relative weight of these two pieces of information. Quantitative experimental results on a large number of gray-level and color images show the improved performance of the proposed approach, in comparison to several related approaches in recent studies. Experiments have also been carried out on real world images in order to validate the proposed method.