NDLI: Training Deformable Part Models with Decorrelated Features

Content Provider	IEEE Xplore Digital Library
Author	Girshick, R. Malik, J.
Copyright Year	2013
Abstract	In this paper, we show how to train a deformable part model (DPM) fast-typically in less than 20 minutes, or four times faster than the current fastest method-while maintaining high average precision on the PASCAL VOC datasets. At the core of our approach is "latent LDA," a novel generalization of linear discriminant analysis for learning latent variable models. Unlike latent SVM, latent LDA uses efficient closed-form updates and does not require an expensive search for hard negative examples. Our approach also acts as a springboard for a detailed experimental study of DPM training. We isolate and quantify the impact of key training factors for the first time (e.g., How important are discriminative SVM filters? How important is joint parameter estimation? How many negative images are needed for training?). Our findings yield useful insights for researchers working with Markov random fields and part-based models, and have practical implications for speeding up tasks such as model selection.
Sponsorship	IEEE Comput. Soc.
Starting Page	3016
Ending Page	3023
File Size	358294
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.375
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines Vectors Computational modeling Deformable models Covariance matrices Acceleration
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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