Mixed-Norm Regression for Visual Classification

Publisher:
Springer
Publication Type:
Journal Article
Citation:
Lecture Notes in Computer Science, 2013, 8346 pp. 265 - 276
Issue Date:
2013-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2013004406OK.pdf2.58 MB
Adobe PDF
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 highdimensional 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.
Please use this identifier to cite or link to this item: