Locality-constrained Linear Coding for image classification
University of Illinois Urbana-Champaign · NEC (United States)
Abstract
The traditional SPM approach based on bag-of-features (BoF) requires nonlinear classifiers to achieve good image classification performance. This paper presents a simple but effective coding scheme called Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM. LLC utilizes the locality constraints to project each descriptor into its local-coordinate system, and the projected coordinates are integrated by max pooling to generate the final representation. With linear classifier, the proposed approach performs remarkably better than the traditional nonlinear SPM, achieving state-of-the-art performance on several benchmarks. Compared with the sparse coding strategy [22], the…
Citation impact
- FWCI
- 182.00
- Percentile
- 100%
- References
- 34
Authors
6- JWJinjun WangCorresponding
- JYJianchao Yang
University of Illinois Urbana-Champaign
- KYKai Yu
NEC (United States)
- FLFengjun Lv
NEC (United States)
- TSThomas S. Huang
University of Illinois Urbana-Champaign
Topics & keywords
- Locality
- Computer science
- Coding (social sciences)
- Pooling
- Artificial intelligence
- Nonlinear system
- Algorithm
- Classifier (UML)