This paper presents a codebook learning approach for image classification and retrieval. It corresponds to learning a weighted similarity metric to satisfy that the weighted similarity between the same labeled images is larger than that between the differently labeled images with largest margin. We formulate the learning problem as a convex quadratic programming and adopt alternating optimization to solve it efficiently. Experiments on both synthetic and real datasets validate the approach. The codebook learning improves the performance, in particular in the case where the number of training examples is not sufficient for large size codebook.
Cai et al. REF apply metric learning to learn a weighted codebook.
Learning weights for codebook in image classification and retrieval
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Computer Science