TY - GEN
T1 - A generalized probabilistic framework for compact codebook creation
AU - Liu, Lingqiao
AU - Wang, Lei
AU - Shen, Chunhua
PY - 2011
Y1 - 2011
N2 - Compact and discriminative visual codebooks are preferred in many visual recognition tasks. In the literature, a few researchers have taken the approach of hierarchically merging visual words of a initial large-size code-book, but implemented this idea with different merging criteria. In this work, we show that by defining different class-conditional distribution function and parameter estimation method, these merging criteria can be unified under a single probabilistic framework. More importantly, by adopting new distribution functions and/or parameter estimation methods, we can generalize this framework to produce a spectrum of novel merging criteria. Two of them are particularly focused in this work. For one criterion, we adopt the multinomial distribution to model each object class, and for the other criterion we propose a max-margin-based parameter estimation method. Both theoretical analysis and experimental study demonstrate the superior performance of the two new merging criteria and the general applicability of our probabilistic framework.
AB - Compact and discriminative visual codebooks are preferred in many visual recognition tasks. In the literature, a few researchers have taken the approach of hierarchically merging visual words of a initial large-size code-book, but implemented this idea with different merging criteria. In this work, we show that by defining different class-conditional distribution function and parameter estimation method, these merging criteria can be unified under a single probabilistic framework. More importantly, by adopting new distribution functions and/or parameter estimation methods, we can generalize this framework to produce a spectrum of novel merging criteria. Two of them are particularly focused in this work. For one criterion, we adopt the multinomial distribution to model each object class, and for the other criterion we propose a max-margin-based parameter estimation method. Both theoretical analysis and experimental study demonstrate the superior performance of the two new merging criteria and the general applicability of our probabilistic framework.
UR - http://www.scopus.com/inward/record.url?scp=80052875474&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2011.5995628
DO - 10.1109/CVPR.2011.5995628
M3 - Conference contribution
SN - 9781457703942
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 1537
EP - 1544
BT - 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011
PB - IEEE Computer Society
ER -