NDLI: A Hierarchical Object Recognition System Based on Multi-scale Principal Curvature Regions

Content Provider	IEEE Xplore Digital Library
Author	Wei Zhang Hongli Deng Dietterich, T.G. Mortensen, E.N.
Copyright Year	2006
Description	Author affiliation: Sch. of Electr. Eng. & Comput. Sci., Oregon State Univ., Corvallis, OR (Wei Zhang; Hongli Deng; Dietterich, T.G.; Mortensen, E.N.)
Abstract	This paper proposes a new generic object recognition system based on multi-scale affine-invariant image regions. Image segments are obtained by a watershed transform of the principal curvature of a contrast enhanced image. Each region is described by an intensity-based statistical descriptor and a PCA-SIFT descriptor. The spatial relations between regions are represented by a cluster-index distribution histogram. With these new descriptors, we develop a hierarchical object recognition system which uses an improved boosting feature selection method (Opelt et al., 2004) to construct layer classifiers by automatically selecting the most discriminative features in each layer. All layer classifiers are then combined to give the final classification. This system is tested on various object recognition problems. Experimental results show that the new hierarchical system outperforms the comparable solutions on most of the datasets tested
Sponsorship	IEEE CPS
Starting Page	778
Ending Page	782
File Size	773365
Page Count	5
File Format	PDF
ISBN	0769525210
ISSN	10514651
DOI	10.1109/ICPR.2006.1195
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Object recognition Boosting System testing Image converters Color Computer science Image segmentation Histograms Hierarchical systems Robustness
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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