Image classification using adapted codebook

Bag of visual words model deriving from text categorization has recently appeared promising for object and image classification, this method always need to deal with large database. This paper proposed an efficient clustering algorithm to obtain universal codebook and adapted codebook, our combination of k-means and agglomerative clustering gives significant improvement in time efficiency while maintaining the same performance of image classification. We also use the adapted codebook to improve image classification performance, an image is presented by a set of histograms - one per class, each histogram describes whether the image is best modeled by the universal codebook or the corresponding adapted class codebook. The experiment result on Caltech-256 shows the combined universal codebook and adapted class codebook representation outperforms those approaches which use the universal codebook only.