Discriminative sparsity preserving projections for image recognition
Discriminative sparsity preserving projections for image recognition
复制标题
用于图像识别的判别稀疏性保留投影
DOI：
10.1016/j.patcog.2015.02.015
复制
发表时间：
2015-08-01
影响因子：
8
通讯作者：
Wang, Yong
中科院分区：
文献类型：
--
作者：
Gao, Quanxue;Huang, Yunfang;Wang, Yong
以前的工作已经证明，图像分类性能可以显着提高流形学习。然而，流形学习的性能很大程度上依赖于参数的手动选择，导致在现实应用中的适应性较差。在本文中，我们提出了一种新的降维方法称为判别稀疏保持投影（DSPP）。与现有的稀疏子空间算法手动构造惩罚邻接图不同，DSPP采用稀疏表示模型自适应地构造固有邻接图和带有权值矩阵的惩罚图，并将全局类内结构集成到判别流形学习目标函数中进行降维。在四个图像数据库上的实验结果证明了该方法的有效性。(C)2015爱思唯尔有限公司版权所有。
Previous works have demonstrated that image classification performance can be significantly improved by manifold learning. However, performance of manifold learning heavily depends on the manual selection of parameters, resulting in bad adaptability in real-world applications. In this paper, we propose a new dimensionality reduction method called discriminative sparsity preserving projections (DSPP). Different from the existing sparse subspace algorithms, which manually construct a penalty adjacency graph, DSPP employs sparse representation model to adaptively build both intrinsic adjacency graph and penalty graph with weight matrix, and then integrates global within-class structure into the discriminant manifold learning objective function for dimensionality reduction. Extensive experimental results on four image databases demonstrate the effectiveness of the proposed approach. (C) 2015 Elsevier Ltd. All rights reserved.