基于非监督决策树的模糊图割模型的彩色图像分割研究

项目名称： 基于非监督决策树的模糊图割模型的彩色图像分割研究

项目编号： No.61502396

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 计算机科学学科

项目作者： 尹诗白

作者单位： 西南财经大学

项目金额： 20万元

中文摘要： 非监督彩色图像分割被广泛应用于工业、农业、医学等领域的机器视觉中，是智能化生产的重要手段。传统的层次化图割模型虽能满足不同领域场景的多样化分割需求，但未考虑高效的模型结构和图像的模糊特性，存在多标签求解NP难，复杂度高，计算量大，硬划分等缺点。本项目将决策树的层次化分类模型与模糊分割理论相结合来解决上述问题。首先，分析分割对象、分割依据、收敛策略对层次化图割算法的效率和结果影响，构建针对彩色图像分割的非监督决策树的模糊图割模型；其次，利用模糊理论指导颜色和纹理特征的提取和融合以及模糊图割的设计和实现，建立基于局部和整体模型的参数优化机制，实现各模块参数之间的协调匹配；最后在仿真图像和真实图像上进行了大量测试。项目拟研究解决层次化图割问题的高效方法，为非监督彩色图像分割提供新概念、新原理和新方法，同时也将推动非监督图割，模糊分割和层次化分割等方面的自身发展。

中文关键词： 彩色图像分割；图割；模糊划分

英文摘要： Unsupervised color image segmentation is widely applied in the machine vision of industry, agriculture, medicine fields, and it is also an important method for intelligent production. Although the traditional methods working with the model of the hierarchical graph cut can meet various segmentation requirements in different scenes of fields, they still has some problems like NP-hard, high complexity, heavy computation, rigid segmentation and so on, due to ignore the effective structure of model and the fuzzy characteristics of the images. Combining hierarchical classification model of the decision tree with the theory of fuzzy segmentation, this project can solve those problems. First of all, we analyze the impact between objects, foundation, convergence and segmentation results, then we develop unsupervised decision tree model of fuzzy graph cut for color image segmentation; Secondly, we utilize the fuzzy theory to guide the extraction and fusion process of color and texture feature, as well as the implementation of fuzzy graph cut; Finally, we build the parameter optimization mechanism of the model, and realize the coordinated optimization. The research results will be tested by plenty of simulated images and real images. In this project, we will develop novel efficient algorithms for solving above-mentioned segmentation problems, and contribute to new concept, theory and methods for unsupervised color image segmentation. In addition, this project will promote the development of unsupervised graph cut, fuzzy segmentation and hierarchical segmentation.

英文关键词： Color Image Segmentation;Graph Cut;Fuzzy Partition