Image Classification Using Adapted Codebook

摘要：

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.

作者: CHENGZHU LIN SHAOZI LI SONGZHI SU

作者单位: Department of Cognitive Science, Xiamen University, Xiamen, 361005, China

会议类型: 国际会议

会议名称: 2009 IEEE International Symposium on IT in Medicine & Education( IEEE 教育与医药信息化国际会议)

会议地点: 济南

会议语种:英文

页码: 1307-1312

在线出版日期: 2009-08-14（万方平台首次上网日期，不代表论文的发表时间）