基于图嵌入的稀疏非负矩阵分解研究及其在特征提取中的应用

项目名称： 基于图嵌入的稀疏非负矩阵分解研究及其在特征提取中的应用

项目编号： No.61203243

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 自动化学科

项目作者： 万鸣华

作者单位： 南昌航空大学

项目金额： 26万元

中文摘要： 在图像识别中，由于计算机对图像的理解和感知能力远逊于人类且处理效率远不能满足当今社会的发展需求，如何提取关键特征进行维数压缩与识识别是当前研究的一个难点与热点问题。本项目主要将人类感知图像的稀疏性机制、非负矩阵分解与流形学习的图嵌入方法研究结合起来，丰富和发展模式识别的特征提取技术理论体系；在技术上设计出具有稀疏性、局部性、鉴别能力和鲁棒性的图像特征选择和图像特征提取算法，提高计算机对图像的理解和感知能力，为图像自动识别在信息及相关领域的应用提供更好的技术支撑。本项目研究的内容包括: (1)在图嵌入方法基础上深入分析当前稀疏表示和非负矩阵分解，提出基于图嵌入的稀疏非负矩阵分解线性模型特征提取算法; (2)将上述研究成果扩展到Hilbert空间和张量数据空间，分别建立基于图嵌入的稀疏非负矩阵分解核模型和张量模型特征提取算法; (3)将上述三个模型特征提取算法用于解决复杂情况下的人脸识别问题。

中文关键词： 图嵌入；稀疏表示；子空间学习；特征抽取；人脸识别

英文摘要： Since computer image understanding and perception far less than human and processing efficiency can not meet the development needs of today's society, how to extract the key features for dimensionality reduction and recognition is a hot topic with difficulties in current researches for image recognition. The projection mainly will be combining sparse mechanisms of human perception image, non-negative matrix factorization and graph embedding of manifold learning to enrich and develop the theoretical system of pattern recognition, feature extraction techniques; Designing the image feature selection and image feature extraction algorithm with sparse, localized, ability to differentiate and robustness to improve the understanding and perception of computer image and to provide better technical support for image recognition in the application of information and related fields.The expected results of this project is based on graph embedding in-depth analysis of the sparse representation:(1)non-negative matrix factorization to provide sparse non-negative matrix factorization based on graph embedding linear model of features extraction algorithm; (2) On this basis, the above research results are extended to the Hilbert space and tensor data space to establish the diagram of sparse non-negative matrix factorization based

英文关键词： Graph embedding；Sparse representation；Subspace learning；Feature extraction；Face recognition