A Boosting, Sparsity- Constrained Bilinear Model for Object Recognition

Authors:
Chunjie Zhang;Jing Liu;Qi Tian;Yanjun Han;Hanqing Lu;Songde Ma
Affiliations:
National Lab of Pattern Recognition, Chinese Academy of Sciences;National Lab of Pattern Recognition, Chinese Academy of Sciences;University of Texas at San Antonio;Douban;National Lab of Pattern Recognition, Chinese Academy of Sciences;National Lab of Pattern Recognition, Chinese Academy of Sciences
Venue:
IEEE MultiMedia
Year:
2012

Citing 0
Cited 4

Image classification using Harr-like transformation of local features with coding residuals

Signal Processing
Beyond bag of words: image representation in sub-semantic space

Proceedings of the 21st ACM international conference on Multimedia
Undo the codebook bias by linear transformation for visual applications

Proceedings of the 21st ACM international conference on Multimedia
Laplacian affine sparse coding with tilt and orientation consistency for image classification

Journal of Visual Communication and Image Representation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Using higher-level visual elements to represent images, the authors have developed a sparsity-constrained bilinear model (SBLM) and have combined a set of SBLMs in a boosting-like procedure to enhance performance.