Research On Texture Modeling And Graph Cuts Optimization Methods

Interactive image segmentation is often described as the process of separating an image into different regions with special semantics according to some prior knowledge and similarity criterion, and then extracting the foreground objects of interest in the complex background environment. It has found a very wide range of applications, which is a key problem in the research for image analysis, pattern recognition, computer vision, and even image understanding. The quality and efficiency of segmentation will have a direct impact on the usability and practicability of other related applications.Due to the significance and complexity of image segmentation, the research about Graph Cuts optimization methods has attracted wide attention by scholars both at home and abroad in recent years, and has become one of the most popular methods used for the application of interactive image segmentation. It has many excellent features, such as ability to fuse a wide range of visual cues and constraints, global optima, numerical robustness, practically efficiency, unrestricted topological properties of weighted graph for segments, and applicability to N-D image segmentation etc. The traditional Graph Cuts optimization methods have good segmentation performance and real-time interaction when segmenting the simple color images with low resolution. However, whether the applicability, accuracy, robustness, or the real-time property of these methods, they all severely limit the usability of these methods when applying them for the complex texture image segmentation, multiple prior pattern features based hybrid intelligent segmentation, or high resolution images based fast segmentation. To address the above problems, this work provides a deeper summarization, analysis and study about the traditional image segmentation methods, especially the Graph Cuts optimization based interactive image segmentation methods, and the main methods used for modeling texture features. Additionally, a new texture modeling method used for extracting the scene structure features is proposed. By combining with the information theory, Riemannian geometry, pattern classification, and numerical analytic method, and making full use of the statistical learning methods and prior heuristic information, this work also researches on how to analyze and interactively segment the color and texture images with high quality, high amount, and high efficiency. In details, the main innovative research achievements of this work can be described as follows.Firstly, an effective texture feature modeling method (multi-scale nonlinear structure tensor, MSNST) is proposed based on the multi-scale nonlinear modeling of the traditional structure tensor. MSNST has both the omni-directional compression description ability of structure tensor and the description ability of Gabor wavelet transform in scale space. Meanwhile, it has the property of filtering in maintaining discontinuity, and provides a completely new idea for the research of texture analysis. In order to improve the efficiency and practicability of the texture extraction, this work uses the "a trous" algorithm and additive operator splitting technique to reduce the complexity of the model's numerical implementation. In addition, by means of the mathematical analysis and experimental comparison, this work studies and discusses some concepts and methods of MSNST feature, which includes dissimilarity measure, probability correlation in scale space, space structure of probability distribution model, and similarity clustering, etc.Secondly, for several parts of the Graph Cuts model framework, this work presents some optimization designs, which detailly include the acceleration of optimization process (fast optimization and solution of models based on multi-seeds Graph Cuts, fast Graph Cuts algorithm based on multi-level banded closed-form, fast Graph Cuts algorithm based on mean shift pre-segmentation and Gaussian super-pixel);high discrimination dissimilarity measure based n-link design (conjugate measure in L*a*b* color space, information theory measure and Riemannian measure in MSNST texture space); high accuracy clustering based t-link design (improved K-means clustering, spectral decomposition based recursive clustering, component-wise expectation-maximization for Gaussian mixtures clustering); computing the Gaussian mixture model (GMM) statistics of foreground and background in each feature space, and then designing the adaptive integration strategy of energy functions for multiple kinds of features based on the information theory distance between GMMs; computing the comprehensive information theory dissimilarity of foreground GMM and background GMM in each feature space during iterating, and then improving the iteration convergence criterion by estimating whether it is stabilizing; enhancing the regional uniformity of segmentation results (including denoising constant in the smoothing energy term, morphological post-processing based denoising) and so on. These optimization designs provide some ideas for enhancing the other Graph Cuts framework based methods and applications, and help them improve the performance and applicability.Finally, based on the above MSNST texture modeling method and all the optimization designs of Graph Cuts model, this work proposes five practical interactive color and texture image segmentation methods, which solve many problems of the traditional segmentation methods. For example, single pattern feature information, simple choices of dissimilarity measure, low accuracy of learning and updating the statistical model, high computational complexity and memory consumption, many user interactions and parameter settings. However, the proposed segmentation methods provide better support for the segmentation of complex real natural scene images, and have the advantages in terms of wide range of applications, powerful discriminative ability, accurate statistical description, low computational burden, and little user dependent.In this dissertation, a large number of simulation experiments have been presented to verify the practicability and usability of multi-scale nonlinear structure tensor model, optimization and improvement methods of the Graph Cuts framework, and color and texture image segmentation techniques. These research achievements can not only enforce the related study in texture analysis and Graph Cuts optimization, but also extend the application prospect of image segmentation in both civilian and military domains.