Multi-view multi-sparsity kernel reconstruction for multi-class image classification

作者：Zhu, Xiaofeng; Xie, Qing; Zhu, Yonghua; Liu, Xingyi; Zhang, Shichao^*

来源：Neurocomputing, 2015, 169: 43-49.

DOI：10.1016/j.neucom.2014.08.106

• Image classification
• Multi-view classification
• Sparse coding
• Structure sparsity
• Reproducing kernel Hilbert space

This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classification rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms.

• 出版日期2015-12-2
• 单位广西大学; 广西师范大学; KAUST; 西安交通大学