基于约束条件的非负矩阵分解算法及其在纤维自动识别中的应用研究

项目名称： 基于约束条件的非负矩阵分解算法及其在纤维自动识别中的应用研究

项目编号： No.61472075

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 其他

项目作者： 万燕

作者单位： 东华大学

项目金额： 81万元

中文摘要： 图像表示是模式识别研究中关键问题之一。非负矩阵分解是一种有效的图像表示方法，近年来已被广泛应用到计算机视觉、信号处理、模式识别和图像处理等领域。但是，非负矩阵分解在处理高维数据时的效率瓶颈以及无法同时考虑样本类别信息和固有几何结构信息的缺陷制约了非负矩阵分解的应用范围和应用研究的发展。本项目针对非负矩阵分解存在的局限性，通过系统地研究基于约束条件的新特性非负矩阵分解模型在维数约减、特征提取和分类识别等关键技术中的应用，以解决非负矩阵分解存在的数据冗余性较大、无监督训练过程不利于后续识别率提高等缺陷，拓展和推动非负矩阵分解的应用，具有重要的理论研究意义和实用价值。同时，将研究成果用于解决混纺纤维的纤维自动分类问题，对纺织品截面纤维进行准确的图像表示，并开发完成基于显微图像的纤维混纺织品自动检测系统。其研究成果将为解决纺织品检验领域纤维自动识别与分析这一世界性难题带来创新性的突破。

中文关键词： 有约束的非负矩阵分解；空间金字塔模型；字典学习；特征编码；纤维识别

英文摘要： Non-negative matrix factorization (NMF) has been widely employed in computer vision and pattern recognition fields since the learned bases can be interpreted as a natural parts-based representation of the input space, which is consistent with the psychological intuition of combining parts to form a whole. It has been shown that learning performance could be significantly enhanced if the geometrical structure was exploited and the local invariance was considered. In addition, the labels are incorporated into the dictionary learning stage and obtained a discriminative dictionary. It gives us a possible that we can incorporate into the NMF model both intrinsic geometrical structure and discriminative information which have been essentially ignored in prior works. Specifically, both the graph Laplacian and supervised label information are jointly utilized to learn the projection matrix in the new model. As one of two significant steps in object recognition, Feature Coding is more important than Dictionary Learning. A dictionary can be produced by the k-means clustering algorithm, and in the coding stage, each descriptor was represented by a nearest basis vector, in which there is only one nonzero entry in each coefficient vector (i.e. sparse matrix). And then each descriptor can be encoded by applying soft inner product coding scheme. We propose to combine the sparse representation of object (fiber) with spatial information of image, which are also important, however, existing NMF methods do not take these two aspects into consideration simultaneously. The spatial information of fiber (the local structure of it) and sparse representation of image can be used together in the spatial pyramid matching (SPM) model for fiber recognition, which has not been applied in the fiber recognition to our knowledge. As far as we know, different types of fibers, such as cotton, flax, and synthetic fibers, are often blended together in textile manufacturing to improve performances of end-use products. The proportion of each component plays an important role in determining the ultimate quality on the end product, and thus needs to be monitored quantitatively for quality inspection. An automatic, fast, accurate fiber component analysis method instead of traditional way is not only the urgent needed by China's inspection and quarantine departments as well as enterprises, but also is one of the worldwide technical difficulties in the commodity inspection field. We propose a research project that applies the constrained NMF technology to automatic identifications of blended fibers. The accuracy, objectivity and testing efficiency will be improved by our inspection work, and a greater social and economic benefit can be brought to our country's textile exports, as well as the monitoring unit to supervise the importation of new fiber, textiles and clothing.

英文关键词： Constrained Non-negative Matrix Factorization;Spatial Pyramid Matching model;Dictionary Learning;Feature Coding;Fiber Classification