Guan-Lin Li, Jia-Shu Wang, Chen-Ru Liao, Chun-Yi Tsai, and Horng-Chang Yang, National Taitung University, Taiwan, R.O.C.

This paper proposes a multiclass recognition scheme which uses multiple feature trees with an extended scoring method evolved from TF-IDF. Feature trees consisting of different feature descriptors such as SIFT and SURF are built by the hierarchical k-means algorithm. The experimental results show that the proposed scoring method combing with the proposed multiple feature trees yields high accuracy for multiclass recognition and achieves significant improvement compared to methods using a single feature tree with original TF-IDF.

SIFT, SURF, K-means, TF-IDF