计算机与现代化Issue(7)：73-76,4.DOI:10.3969/j.issn.1006-2475.2015.07.016
基于分布结构约束稀疏表示的图像分类方法
Image Classification Method Based on Distribution Structure Constrain Sparse Representation
范引娣1
作者信息
- 1. 陕西交通职业技术学院经济管理系，陕西 西安 710018
- 折叠
摘要
Abstract
To solve the structure information loss issue on sparse representation for accurate image classification, a new method based on structure constrain sparse representation was proposed. The training samples after downsampling and extracting histo-gram of orientated gradient ( Hog) were utilized to construct sparse linear coding model. The sparse coefficients were solved on the training samples by distribution structure information constrain andℓ1-minimization, and image was classified by sparse coeffi-cient mean. Experimental results with COREL dataset demonstrated that the proposed method can obtain the good recognition per-formance. Comparing with non-structure constrain sparse representation, the proposed method greatly improves the accuracy of image classification.关键词
结构约束/稀疏表示/图像分类Key words
distribution structure constrain/sparse representation/image classification分类
信息技术与安全科学引用本文复制引用
范引娣..基于分布结构约束稀疏表示的图像分类方法[J].计算机与现代化,2015,(7):73-76,4.
