Locality-constrained Linear Coding for Image Classification

The traditional SPM approach based on bag-of-features (BoF) must use 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 objective function used by LCC has an analytical solution, bearing much lower computational complexity of O(M + M) with M the size of codebook. In addition, the paper proposes an approximated LCC method by first performing a K-nearest-neighbor search and then solving a constrained least ...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Jinjun Wang, Jianchao Yang, Kai Yu, Fengjun Lv
• Keywords: Computer Vision, CVPR 2010, Traditional Nonlinear Spm, Traditional Spm, Traditional Spm Approach