An algorithm for unsupervised color image segmentation

Segmentation is a low-level processing aimed at the partition of an image in disjoint regions, each one homogeneous with respect to some properties like intensity, texture, shape, etc. Such a task is needed in many high-level processing and applications in such diverse fields as remote-sensing, medical imaging, video coding, and industrial automation, just to name a few. In this work of thesis, we first deal with the problem of color based image segmentation, that is based on some form of homogeneity in the color/spectral properties of the image, and then move to the more complex task of texture based segmentation, where the aim is to recognize complex structures in the image which are typically non homogeneous in terms of spectral properties. Concerning color based segmentation, we focus on a specific class of Markov Random Field (MRF) probability models, namely the Tree-Structured Markov Random Fields (TS-MRF), recently proposed to provide a hierarchical multiscale description of images. Based on such a model, the unsupervised image segmentation is carried out by means of a sequence of nested class splits, where each class is modeled as a local binary MRF. As a first contribution, we first extend the model to generic tree structures removing the unnecessary binary constraint, and then devise a new TS-MRF unsupervised segmentation technique which improves upon the original algorithm by selecting a better tree structure and eliminating spurious classes. Such results are obtained by using the Mean-Shift procedure to estimate the number of pdf modes at each node, and to obtain a more reliable initial clustering for subsequent MRF optimization. To this end, we devise a new reliable and fast clustering algorithm based on the Mean-Shift technique, which makes use of a variable-bandwidth strategy based on the k-Nearest Neighbors (k-NN) technique, and is implemented with a speed-up strategy that cuts significantly the computational complexity. Experimental results on synthetic data and real life images from the remote sensing domain prove the potential of the proposed method. Turning to texture based segmentation, 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 images from the Berkeley dataset. Finally, an application of the TFR algorithm for the unsupervised segmentation of multiresolution remotely sensed images is proposed, based on the significant presence of textural content in last generation high-resolution satellite images. The obtained results further assess the potential of the proposed method for such real life applications.

In this paper, we present a new unsupervised colour image segmentation algorithm using competitive and morphological concepts. The algorithm is carried out in three processing stages. It starts by an estimation of the density function, followed by a training competitive neural network with a new criterion of resemblance called Mahalanobis distance which detects local maxima of the density function, and ends by the extraction of modal regions using an original method based on the morphological concept. The so detected modes are then used for the classification process. Compared to the K-means clustering or to the clustering approaches based on the different competitive learning schemes, the proposed algorithm has proven, under a number of real and synthetic test images, that it is automatic, has a fast convergence and does not need priori information about the data structure.

We present an unsupervised segmentation algorithm which uses Markov random field models for color textures. These models characterize a texture in terms of spatial interaction within each color plane and interaction between different color planes. The models are used by a segmentation algorithm based on agglomerative hierarchical clustering. At the heart of agglomerative clustering is a stepwise optimal merging process that at each iteration maximizes a global performance functional based on the conditional pseudolikelihood of the image. A test for stopping the clustering is applied based on rapid changes in the pseudolikelihood. We provide experimental results that illustrate the advantages of using color texture models and that demonstrate the performance of the segmentation algorithm on color images of natural scenes. Most of the processing during segmentation is local making the algorithm amenable to high performance parallel implementation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The topic of colour image segmentation has been and still is a hot issue in areas such as computer vision and image processing because of its wide range of practical applications. The urge has led to the development of numerous colour image segmentation algorithms to extract salient objects from colour images. However, because of the diverse imaging conditions in varying application domains, accuracy and robustness of several state-of-the-art colour image segmentation algorithms still leave room for further improvement. This dissertation reports on the development of a new image segmentation algorithm based on perceptual colour difference saliency along with binary morphological operations. The algorithm consists of four essential processing stages which are colour image transformation, luminance image enhancement, salient pixel computation and image artefact filtering. The input RGB colour image is first transformed into the CIE L*a*b colour image to achieve perceptual saliency and obtain the best possible calibration of the transformation model. The luminance channel of the transformed colour image is then enhanced using an adaptive gamma correction function to alleviate the adverse effects of illumination variation, low contrast and improve the image quality significantly. The salient objects in the input colour image are then determined by calculating saliency at each pixel in order to preserve spatial information. The computed saliency map is then filtered using the morphological operations to eliminate undesired factors that are likely present in the colour image. A series of experiments was performed to evaluate the effectiveness of the new perceptual colour difference saliency algorithm for colour image segmentation. This was accomplished by testing the algorithm on a large set of a hundred and ninety images acquired from four distinct publicly available benchmarks corporal. The accuracy of the developed colour image segmentation algorithm was quantified using four widely used statistical evaluation metrics in terms of precision, F-measure, error and Dice. Promising results were obtained despite the fact that the experimental images were selected from four different corporal and in varying imaging conditions. The results have indeed demonstrated that the performance of the newly developed colour image segmentation algorithm is consistent with an improved performance compared to a number of other saliency and non- saliency state-of-the-art image segmentation algorithms.

In this paper, an unsupervised segmentation method using clustering is presented for color images. We propose to use a neural network based approach to automatic feature selection to achieve adaptive segmentation of color images. With a self-organizing feature map (SOFM), multiple color features can be analyzed, and the useful feature sequence (feature vector) can then be determined. The encoded feature vector is used in the final segmentation using fuzzy clustering. The proposed method has been applied in segmenting different types of color images, and the experimental results show that it outperforms the classical clustering method. Our study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.

In this paper, an unsupervised image segmentation algorithm is proposed, which combines spatial constraints with a kernel fuzzy c-means (KFCM) clustering algorithm. Conventional KFCM clustering segmentation algorithm does not incorporate the spatial context information of image, which makes it sensitive to the noise and intensity variations. In order to overcome the shortcomings, the contents of image is characterized by Gaussian mixture model, and the parameters of model are estimated by modified expectation maximization (EM) algorithm, which overcomes the classical EM algorithm drawbacks that easily trap in local maxima and be susceptible to initial value. According to the maximum a posterior theorem, we can get the pixel maximum posterior probability. We redefine the objective function of the KFCM algorithm which incorporates the pixel maximum posteriori probability, by minimizing the fuzzy objective function, the fuzzy segmentation algorithm is derived. The experimental results on a synthetic image and a real magnetic resonance image show that the proposed algorithm is more effective than the conventional FCM algorithm without local spatial constraints.

In this paper an unsupervised colour image segmentation algorithm is presented. This method combines the advantages of the approaches based on split&merge and region growing, and the use of the RGB and HSV colour representation models. The effectiveness of the proposed method has been verified by the implementation of the algorithm using three different testing images with homogeneous regions, spatially compact and continuous. It was observed that the proposed algorithm outperforms the other analysed techniques requiring shorter processing time when compared with the other analysed methods.

Scale-spaces play an important role in many computer vision tasks. Automatic scale selection is at the foundation of multi- scale image analysis, but its performance is still very subjective and emp irical. To automatically select the appropriate scale for a particular issue, a scale selection model based on information theory is pr oposed in this paper. The proposed model utilizes mutual information as a measuring criterion of similarity for the optimal scale selectio n in multi-scale analysis, with applications to image denoising and segmentation. Firstly, we focus on the morphological operator based scale selection to image denoising. This technique does not require the prior knowledge of the noise variance and can effectively eliminate the variation of illumination. Secondly, we develop a clustering based unsupervised image segmentation algorithm by recursively pruning the Huffman coding tree. The proposed clustering algorithm can preserve the maximum amount of information at a specific clustering number from the information-theoretical point of view. Finally, for the feasibility of the proposed algorithms, its theor etical properties are analyzed mathematically and its performance is tested by a series of experiments, which demonstrate that it yields the optimal scale for our developed image denoising and segmentation algorithms.

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.

A novel neural network-based technique for segmentation of single-channel magnetic resonance images is presented. The segmentation of single-channel magnetic resonance images is a daunting task due to the relatively little information available at each pixel site. The proposed algorithm is based on unsupervised clustering by means of a Kohonen Self-Organizing Map: unsupervised segmentation algorithms are highly desirable in order to eliminate intra- and interobserver variability. Particular attention has been devoted to the choice of suitable features, in order to ensure an accurate and reliable segmentation: in particular, a feature set extracted from the neighborhood of each pixel has been evaluated. The proposed technique has been tested on simulated magnetic resonance images to assess its stability against the presence of noise and intensity inhomogeneities. Moreover, it has been tested on real magnetic resonance images of both volunteers and brain tumor patients. The preliminary results presented make the proposed technique a promising alternative for the segmentation of single-channel magnetic resonance images and encourage further investigation.

A Validation Framework for Brain Tumor Segmentation

A new histogram feature based natural image segmentation algorithm has been proposed. The proposed scheme uses histogram based new color texture extraction method which inherently combines color texture features rather then explicitly extracting it. A non parametric Bayesean clustering is employed to make the segmentation framework fully unsupervised where no a priori knowledge about the number and types of regions are required. The performance of the proposed method have been demonstrated by various experiments using images of natural scenes. The experimental results indicates that superior segmentation results can be obtained through the proposed unsupervised natural image segmentation algorithm.

This paper describes an efficient algorithm for color image segmentation based on a multiresolution application of a wavelet transform and watershed segmentation algorithm. The procedure toward complete segmentation consists of four steps: pyramid representation, image segmentation, region projection and region merging. First, pyramid representation creates multiresolution images using a wavelet transform. Second, image segmentation segments the lowest-resolution image of the pyramid using a watershed segmentation algorithm. Third, the segmented low-resolution image with label is projected into a full-resolution image (original image) by inverse wavelet transform. Finally, region merging merges the segmented regions using fuzzy similarity. Experimental results of the presented method can be applied to the segmentation of noise or degraded images as well as reduce over-segmentation.

This paper presents a novel algorithm for unsupervised texture segmentation. The proposed algorithm incorporates the local binary pattern operator under a segmentation framework based on the active contour without edges model. The experiments performed, show that it can be used for fast segmentation of two-textured images, outperforming recent texture segmentation algorithms, with a segmentation quality that reaches 99% on average

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, a hybrid preaching optimization algorithm (HPOA) for color image segmentation is proposed. Firstly, the evolutionary state strategy is adopted to evaluate the evolutionary factors in each iteration. With the introduction of the evolutionary state, the proposed algorithm has more balanced exploration-exploitation compared with the original POA. Secondly, in order to prevent premature convergence, a randomly occurring time-delay is introduced into HPOA in a distributed manner. The expression of the time-delay is inspired by particle swarm optimization and reflects the history of previous personal optimum and global optimum. To better verify the effectiveness of the proposed method, eight well-known benchmark functions are employed to evaluate HPOA. In the interim, seven state-of-the-art algorithms are utilized to compare with HPOA in the terms of accuracy, convergence, and statistical analysis. On this basis, an excellent multilevel thresholding image segmentation method is proposed in this paper. Finally, to further illustrate the potential, experiments are respectively conducted on three different groups of Berkeley images. The quality of a segmented image is evaluated by an array of metrics including feature similarity index (FSIM), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Kapur entropy values. The experimental results reveal that the proposed method significantly outperforms other algorithms and has remarkable and promising performance for multilevel thresholding color image segmentation.