Topic based pose relevance learning in dance archives
Reede Ren, John P. Collomosse, Joemon M. Jose · 2012
This paper improves spatial pyramid kernel (SPK) and proposes a relevance learning approach to compare performer's poses in a large dance archive, the NRCD collection1. Domain knowledge of Choreutics is exploited to define pose topics and a selection operator is developed for pose topic matching. The visual structure descriptor of self similarity (SSF) is extended to hierarchical self similarity (HSSF) to keep shape context. The framework of Bag-of-Visual Words (BOVW) is applied to encode as well as to speed up the matching on pose topics/topic combinations. This alleviates the complexity in limb allocation which is infeasible in our data. Extensive experiments show that the new approach outperforms the original SPK in both precision and robustness.