Kernel-based spectral color image segmentation

This paper proposes a fast approach to spectral image segmentation. In the algorithm, two popular techniques are extended and applied to spectral color images: the mean-shift filtering and the kernel-based clustering. We claim that segmentation should be completed under illuminant F11 rather than directly using the original spectral reflectance, because such illumination can reduce data variability and expedite the following filtering. The modes obtained in the mean-shift filtering represent the local features of spectral images, and will be applied to segmentation in place of pixels. Since the modes are generally small in number, the eigendecomposition of kernel matrices, the crucial step in the kernel-based clustering, becomes much easier. The combination of these two techniques can efficiently enhance the performance of segmentation. Experiments show that the proposed segmentation method is feasible and very promising for spectral color images. Keywords: spectral color image, illuminant F11, mean-shift filtering, spectrum extension, kernel-based clustering

The spectral clustering algorithm's space complexity is O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), while time complexity is O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ). When dealing with large amounts of data, the memory will overflow and run-time is too long. For the general problem of spectral clustering, if the clustering data of sub-problem between the original problem has the same probability distribution, it can be applied to divide and conquer strategy for the problem of spectral clustering, by the spectral clustering results of sub-problems to get the spectral clustering results of original problem. To spectral clustering image segmentation as a research object, we will discuss the divide and conquer strategy for spectral clustering in this paper. Experiments show that the application of divide and conquer method for spectral clustering image segmentation, we can get a perfect performance in image segmentation.

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 recent years, there has been a notable surge in the development of methods dedicated to classifying hyper spectral image samples. However, the inherent high dimensionality of hyper spectral data presents significant challenges for traditional supervised classification algorithms. Prior studies have attempted to address these challenges by incorporating active learning methods for hyper spectral data segmentation. Nevertheless, a crucial drawback of active learning lies in its dependence on labeled data samples, leading to less improved classification results for unlabeled data samples. In response to this limitation, our work introduces Supervised Active Learning (SAL) techniques that harness both spectral and spatial information. The key innovation of our research lies in the introduction of a novel hyper spectral image segmentation approach based on the k-dependence Bayesian (KDB) model. We estimate the maximizer of the posterior marginal using Symmetric Iterative Proportional Fitting (SIPF) within this KDB model. By implementing the KDB model for hyper spectral image segmentation, our goal is to enhance the classification accuracy of our SAL classifiers. The SAL framework integrates shape prior probability estimation, class density results from Improved Multi-Level Logistic (IMLL) methods, and the maximization of the posterior marginal estimated through Symmetric Iterative Proportional Fitting (SIPF). We evaluate the accuracy of our proposed MPM-SIPF-SAL system using the real ROSIS Hyper Spectral Dataset, demonstrating that our system outperforms existing methods in terms of classification accuracy.

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 the image segmentation, the spectral clustering algorithm are facing the problems, that the similarity matrix and Laplace matrix requires a lot of computing and storage, which limits its expansion in the large-scale image. Aiming at these problems, a semi-supervised spectrum clustering algorithm based on the Nystrom approximation was proposed. In the algorithm, adopting the Nystrom approximation method to estimate the similarity matrix, which effectively reduces the amount of computation and storage of the spectral clustering algorithm. Finally, on the Brodatz texture library, the image segmentation experiments showed the feasibility and effectiveness of the algorithm.

Image segmentation method based on the existing spectrum clustering algorithm cannot accurately segment sonar image because of the ambiguous object edge, extremely complex noisy background and critical shadow impact of sonar image, and aiming at this problem, this paper proposed an improved spectrum clustering sonar image segmentation method. First of all, to overcome negative impact of the sonar image shadow and achieve selective segmentation of sonar target, morphological top-hat transformation and bottom-hat transformation were used for sonar image preprocessing; Secondly, image segmentation based on spectrum clustering was done after preprocessing and an improved spectral clustering sonar image segmentation system was constructed; Finally, simulation experiment was taken and the results showed that the proposed method is more suitable for sonar image segmentation.

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.

To overcome over-segmentation of Watershed transform, a novel improved Watershed algorithm based on adaptive marker-extraction is proposed. The original marker-based Watershed algorithm is improved by considering multiple feature information of local minima and adaptively selecting threshold. The proposed method consists of five steps: 1) Calculating gradient directly with color vectors; 2) Low-pass filtering of gradient image with BTPF; 3) Employing Hminima transform to extract true local minima whose depth is lower than that of threshold H, which is adaptively adjusted according to gradient image's statistical character. 4) Further marker-extraction being based on water basin scale. 5) Imposing the markers on the original gradient image as its minima; finally, Watershed transform is implied to the marked gradient image to segment the image. Experimental results show that, compared with other testing Watershed algorithms, the proposed method can more efficiently reduce over-segmentation and obtain better segmentation performance with lower computational complexity; in addition, it has better anti-noise performance and edge-location capability as well.

Simulated annealing spectral clustering algorithm for image segmentation

In a great number of fields of computer vision image segmentation plays an essential role as a preliminary step towards further and higher levels of image processing. An original algorithm for unsupervised segmentation of color images is here presented; it resorts to a palletized representation of images, it uses the low spatial frequency content of color which is represented in the CIELUV space and in this space it builds and thresholds hue and chroma histograms finding out the color clusters which allow the image to be segmented. Evidence of the algorithm's effectiveness is reported.

In this paper, we propose a new color edge model and color edge histogram descriptor for contents-based image retrieval. The edge descriptor proposed by MPEG-7 standard is a representative approach for the contents-based image retrieval using the edge histogram that is derived from a gray-level image. This paper introduces a novel method that extracts color edge information from spectral color images rather than monochrome images and a new color edge histogram descriptor for the contents-based image retrieval. The proposed color edge model is obtained in two phases. In the first phase, we characterize the R,G,B channel components as a linear map (the differential) and impose a statistical interpretation on this map by considering the behavior of the map as it applied to unit normed vectors in the second phase. As a result, the constructed edge model will be expressed in a statistical fashion and will provide a mechanism to determine the possibility of the edge existence. The color edge histogram based on the direction of the color edge model is subsequently applied to the contents-based e-catalogue image retrieval. For the evaluation, the results of image retrieval using the proposed method are compared with those of image retrieval using the edge descriptor by MPEG-7 and other approaches. The experimental result supports the efficiency of the proposed method.KeywordsColor ImageImage RetrievalQuery ImageColor HistogramColor EdgeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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.

This paper studied the key technology of the extraction of urban road information using high-resolution QuickBird image. And this paper discussed the road information extraction algorithms, K-mean image clustering and segmentation algorithm, lambda-schedule image merging algorithm and the construction of knowledge database using spectral and shape features, from three angles, image segmentation algorithm, image merging algorithm and specific road information extraction algorithm. Studies showed that, after the introduction of the spectral and texture information, road extraction method reached a higher area and shape consistency, and provide a valuable reference for related research in the field.

The most important properties of color spaces are without cross contamination between lightness, saturation and hue and with maximum perceptual uniformity after analyzing the deficiency of existing perception-based image processing problems. Unlike the traditional perception-based color spaces, this paper using multi-grid optimization and standard color-difference formulas defined a color lookup tables, constructs a new hue linear color space transformed images from an initial color space to the new color space. In order to verify the validity for image processing of the new space, we compared the color gamut mapping and image segmentation algorithms on the testing images with different color spaces. After comparing with the exiting color spaces, the experiment results showed color attributes of the new space free of cross contamination and with maximum perceptual uniformity which can improve the perception-based image processing algorithms accuracy.