项目名称: 基于稀疏语义表示的大规模图像分类问题研究
项目编号: No.61262050
项目类型: 地区科学基金项目
立项/批准年度: 2013
项目学科: 自动化技术、计算机技术
项目作者: 李波
作者单位: 南昌航空大学
项目金额: 46万元
中文摘要: 本项目拟以近年来在底层图像处理领域取得成功应用的稀疏表示方法为基础,以解决当前图像分类所面临的语义特征表达和大规模等关键问题为目的,重点研究可用于分类的图像鉴别稀疏特征的提取与评价,建立基于稀疏表示的快速图像分类算法,在稀疏表示的框架下来研究图像分类的语义表达与大规模问题。具体的,(1)针对图像的语义表达问题,研究适用于图像分类的保语义鉴别稀疏特征空间的构造与表示,在降低图像分类计算复杂度的同时,保证特征空间的可分离性;(2)针对大规模图像分类问题,研究基于先验信息的稀疏空间构造问题,建立可压缩稀疏空间中的快速图像分类方法;(3)针对图像分类的可扩展性问题,建立面向动态数据库的随机逼近快速图像分类算法。本项目将充分挖掘基于稀疏表示的图像分类新方法,探索新方法能否突破传统分类问题面临的表达与大规模瓶颈,并在中山大学数字医疗媒体库上进行试验验证。
中文关键词: 稀疏表示;低秩表示;特征提取;在线分类;
英文摘要: Inspired by the successful application of sparse representation methods in low-level image processing, this project mainly study the discriminative sparse representation for image classification and the fast classification methods for large-scale problem, aiming at solving the bottleneck of image classification. This project will build the research method of image classification under the framework of sparse representation. The main contribution includes:(1) research of sparse feature space which is discriminative, semantic and adaptive for the application of image classification. While reducing the computer complexity,it should ensure the separability of the feature space;(2) For the large-scale problem,introduce the construction of sparse feature space via priori information , and build the fast classification algorithm in the compressive space;(3) For the dynamic expanded training dataset, build the fast dynamic stochastic learning algorithm by using the main idea of stochastic approximation, which is classical in mathematical optimization. In this project, we will dig up the new methods of image classification with sparse representation, and prob that if the new methods can overcome the now existing bottleneck of image classification, and finally we will make experiments on the medical database provided by
英文关键词: sparse representation;low-rank representation;feature extraction;online classification;