Unsupervised image segmentation using convolutional autoencoder with total variation regularization as preprocessing

The demand for accurate and reliable unsupervised image segmentation methods is high. Regardless of whether we are faced with a problem for which we do not have a usable training dataset, or whether it is not possible to obtain one, we still need to be able to extract the desired information from images. In such cases, we are usually gently pushed towards the best possible clustering method, as it is often more robust than simple traditional image processing methods. We investigate the usefulness of combining two clustering methods for unsupervised image segmentation. We use the mixture models to extract the color and spatial image features based on the obtained output segments. Then we construct a similarity matrix (adjacency matrix) based on these features to perform spectral clustering. In between, we propose a label noise correction using Markov random fields. We investigate the usefulness of our method on many hand-crafted images of different objects with different shapes, colorization, and noise. Compared to other clustering methods, our proposal performs better, with 10% higher accuracy. Compared to state-of-the-art supervised image segmentation methods based on deep convolutional neural networks, our proposal proves to be competitive.

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.

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.

The aim of our paper is to present a new unsupervised Bayesian image segmentation method using a recent model by hidden fuzzy Markov fields. The main problem of parameter estimation is solved using a recent general method of estimation regarding hidden data, called iterative conditional estimation (ICE). This has been successfully applied in classical hidden Markov fields based segmentations. The first part of our work involves estimating the parameters defining the Markovian distribution of the fuzzy picture without noise. We then combine this algorithm with the ICE method in order to estimate all the parameters of the noisy picture.

The work addresses Bayesian unsupervised satellite image segmentation, using contextual methods. It is shown, via a simulation study, that the spatial or spectral context contribution is sensitive to image parameters such as homogeneity, means, variances, and spatial or spectral correlations of the noise. From this one may choose the best context contribution according to the estimated values of the above parameters. The parameter estimation is done by SEM, a densities mixture estimator which is a stochastic variant of the EM (expectation-maximization) algorithm. Another simulation study shows good robustness of the SEM algorithm with respect to different image parameters. Thus, modification of the behavior of the contextual methods, when the SEM-based unsupervised approaches are considered, is limited, and the conclusions of the supervised simulation study stay valid. An adaptive unsupervised method using more relevant contextual features is proposed. Different SEM-based unsupervised contextual segmentation methods, applied to two real SPOT images, give consistently better results than a classical histogram-based method.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Unsupervised brain tumor segmentation using a symmetric-driven adversarial network

Image segmentation is the process of partitioning a digital image into a set of homogeneous regions (according to some homogeneity criterion) to facilitate a subsequent higher-level analysis. In this context, the present paper proposes an unsupervised and graph-based method of image segmentation, which is driven by an application goal, namely, the generation of image segments associated with a user-defined and application-specific goal. A graph, together with a random grid of source elements, is defined on top of the input image. From each source satisfying a goal-driven predicate, called seed, a propagation algorithm assigns a cost to each pixel on the basis of similarity and topological connectivity, measuring the degree of association with the reference seed. Then, the set of most significant regions is automatically extracted and used to estimate a statistical model for each region. Finally, the segmentation problem is expressed in a Bayesian framework in terms of probabilistic Markov random field (MRF) graphical modeling. An ad hoc energy function is defined based on parametric models, a seed-specific spatial feature, a background-specific potential, and local-contextual information. This energy function is minimized through graph cuts and, more specifically, the alpha-beta swap algorithm, yielding the final goal-driven segmentation based on the maximum a posteriori (MAP) decision rule. The proposed method does not require deep a priori knowledge (e.g., labelled datasets), as it only requires the choice of a goal-driven predicate and a suited parametric model for the data. In the experimental validation with both magnetic resonance (MR) and synthetic aperture radar (SAR) images, the method demonstrates robustness, versatility, and applicability to different domains, thus allowing for further analyses guided by the generated products.

Obtaining canopy area, crown width, position, and other information from UAV aerial images and adjusting spray parameters in real-time according to this information is an important way to achieve precise pesticide application in orchards. However, the natural illumination environment in the orchard makes extracting the fruit tree canopy difficult. Hereto, an effective unsupervised image segmentation method is developed in this paper for fast fruit tree canopy acquisition from UAV images under natural illumination conditions. Firstly, the image is preprocessed using the shadow region luminance compensation method (SRLCM) that is proposed in this paper to reduce the interference of shadow areas. Then, use Naive Bayes to obtain multiple high-quality color features from 10 color models was combined with ensemble clustering to complete image segmentation. The segmentation experiments were performed on the collected apple tree images. The results show that the proposed method’s average precision rate, recall rate, and F1-score are 95.30%, 84.45%, and 89.53%, respectively, and the segmentation quality is significantly better than ordinary K-means and GMM algorithms.

Traditional supervised learning methods have limitations in labeling speed, scene adaptability and accuracy.Unsupervised learning methods do not require labeling data and can automatically extract the laws, which provides a new idea for image segmentation, especially in medical diagnosis, automatic driving and other highprecision requirements of the scene has an important application value.This study explores unsupervised learningbased image segmentation methods in computer vision, focusing on the improved kernel fuzzy C-mean clustering algorithm (KFCM).The study constructs an image segmentation algorithm with noise robustness by introducing a kernel function instead of Euclidean distance and combining it with super-pixel segmentation technique.The experiments are validated on synthetic, natural and medical images and compared with various classical algorithms.The results show that when 30% Gaussian noise is added to the synthetic image, the segmentation accuracy of the KFCM algorithm reaches 99.8%, which is 12.6% higher than that of the traditional FCM; in the segmentation of the natural image with the addition of mixed noise, the average value of the segmentation coefficient of the KFCM reaches 96.45%, which is 17.47% higher than that of the FCM, and the segmentation entropy is reduced by 34.57%; and in the segmentation of the medical cell image, the KFCM algorithm shows good edge keeping ability in complex noise environment.The study shows that the improved KFCM algorithm significantly improves the image segmentation accuracy and anti-noise performance through the adaptive neighborhood information and kernel mapping, and provides an effective solution for unsupervised image segmentation, which is of practical application value for medical diagnosis, automatic driving and other fields.

Building segmentation is an important part of urban scene 3D reconstruction and smart city applications. However, current approaches frequently struggle to precisely define boundary details due to the complexity of urban environments and the morphological diversity of buildings, which lowers segmentation precision. Therefore, this paper proposes an unsupervised stereo segmentation method integrating spectral and geometric features. By adding the three-dimensional structure information represented by disparity maps, the proposed method enhances the accuracy and completeness of individual building boundaries. The method first performs initial pre-segmentation within a joint spectral-geometric feature space. Subsequently, to further preserve building integrity, spectral and geometric information are incorporated into region fusion during belief propagation. Finally, geometric information is re-utilised for edge refinement to obtain the segmentation result. Extensive experiments demonstrate that the proposed method significantly outperforms mainstream algorithms in both region and boundary localization. On the WHU-Stereo dataset, the BR score reaches 0.825, representing a 9.0% improvement over the state-of-the-art method. On the ISPRS Vaihingen dataset, the BR score reaches 0.712, yielding a 7.9% improvement compared with the state-of-the-art method.

We propose a novel approach to investigate and implement unsupervised image content understanding and segmentation of color industrial images like medical imaging, forensic imaging, security and surveillance imaging, biotechnical imaging, biometrics, mineral and mining imaging, material science imaging, and many more. In this particular work, our focus will be on medical images only. The aim is to develop a computer aided diagnosis (CAD) system based on a newly developed Multidimensional Spatially Variant Finite Mixture Model (MSVFMM) using Markov Random Fields (MRF) Model. Unsupervised means automatic discovery of classes or clusters in images rather than generating the class or cluster descriptions from training image sets. The aim of this work is to produce precise segmentation of color medical images on the basis of subtle color and texture variation. Finer segmentation of images has tremendous potential in medical imaging where subtle information related to color and texture is required to analyze the image accurately. In this particular work, we have used CIE-Luv and Daubechies wavelet transforms as color and texture descriptors respectively. Using the combined effect of a CIE-Luv color model and Daubechies transforms, we can segment color medical images precisely in a meaningful manner. The evaluation of the results is done through comparison of the segmentation quality with another similar alternative approach and it is found that the proposed approach is capable of producing more faithful segmentation.

Low-key images widely exist in imaging-based systems such as space telescopes, medical imaging equipment, machine vision systems. Unsupervised low-key image segmentation is an important process for image analysis or digital measurement in these applications. In this paper, a novel active contour model with the probability density function (PDF) of gamma distribution for image segmentation is proposed. The flexible gamma distribution is used to describe both of the heterogeneous foreground and dark background in a low-key image. Besides, an unsupervised curve initialization method is also designed in this paper, which helps to accelerate the convergence speed of curve evolution. The effectiveness of the proposed algorithm is demonstrated through comparison with the CV model. Finally, an industrial application based on proposed approach is described in this paper.

In this paper a new unsupervised nonparametric cooperative adaptive hyperspectral image segmentation approach is presented. In the approach the images are processed band by band, and then the results of all bands are fused, to get the final segmentation result. Two unsupervised nonparametric segmentation methods are cooperated, namely Fuzzy C-means (FCM), and Linde-Buzo-Gray (LBG) to segment each band in the image. The originality of the approach relies firstly on, its adaptation to the types of regions in an image (textured, non-textured), secondly, on the validation approach, which is done in two levels: direct validation, and validation by genetic fusion. The approach is tested on different mono-band and multi-band images and shown its efficiency.

Breast lesion segmentation in magnetic resonance (MR) images is one of the most important parts of clinical diagnostic tools. Pixel classification methods have been frequently used in image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to be obtained. On the other hand, unsupervised segmentation methods need no prior knowledge and lead to low performance. However, semi-supervised learning which uses not only a few labeled data, but also a large amount of unlabeled data promises higher accuracy with less effort. In this paper, we propose a new interactive semi-supervised approach to segmentation of suspicious lesions in breast MRI. Using a suitable classifier in this approach has an important role in its performance; in this paper, we present a semi-supervised algorithm improved self-training (IMPST) which is an improved version of self-training method and increase segmentation accuracy. Experimental results show that performance of segmentation in this approach is higher than supervised and unsupervised methods such as K nearest neighbors, Bayesian, Support Vector Machine, and Fuzzy c-Means.

A novel unsupervised multi-spectral multiple-segmenter texture segmentation method with unknown number of classes is presented. The unsupervised segmenter is based on a combination of several unsupervised segmentation results, each in different resolution, using the sum rule. Multi-spectral texture mosaics are locally represented by four causal multi-spectral random field models recursively evaluated for each pixel. The single-resolution segmentation part of the algorithm is based on the underlying Gaussian mixture model and starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached. The performance of the presented method is extensively tested on the Prague segmentation benchmark using the commonest segmentation criteria and compares favourably with several alternative texture segmentation methods.