NDLI: A generalized probabilistic framework for compact codebook creation

Content Provider	IEEE Xplore Digital Library
Author	Lingqiao Liu Lei Wang Chunhua Shen
Copyright Year	2011
Description	Author affiliation: School of Engineering, Australian National University, ACT 0200, Australia (Lingqiao Liu; Lei Wang) \|\| Australian Center Visual Technologies, University of Adelaide, SA 5005, Australia (Chunhua Shen)
Abstract	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.
Starting Page	1537
Ending Page	1544
File Size	369168
Page Count	8
File Format	PDF
ISBN	9781457703942
ISSN	10636919
e-ISBN	9781457703959
DOI	10.1109/CVPR.2011.5995628
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Merging Maximum likelihood estimation Probabilistic logic Training Support vector machines Histograms
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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