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  Combining Local and Global Image Features for Object Class Recognition

Lisin, D., Mattar, M., Blaschko, M., Benfield, M., & Learned-Miller, E. (2005). Combining Local and Global Image Features for Object Class Recognition. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05): Workshops (pp. 1-8). Los Alamitos, CA, USA: IEEE.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D55D-C Version Permalink: http://hdl.handle.net/21.11116/0000-0005-38C4-F
Genre: Conference Paper

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 Creators:
Lisin, DA, Author
Mattar, MA, Author
Blaschko, MB1, Author              
Benfield, MC, Author
Learned-Miller, EG, Author
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: Object recognition is a central problem in computer vision research. Most object recognition systems have taken one of two approaches, using either global or local features exclusively. This may be in part due to the difficulty of combining a single global feature vector with a set of local features in a suitable manner. In this paper, we show that combining local and global features is beneficial in an application where rough segmentations of objects are available. We present a method for classification with local features using non-parametric density estimation. Subsequently, we present two methods for combining local and global features. The first uses a "stacking" ensemble technique, and the second uses a hierarchical classification system. Results show the superior performance of these combined methods over the component classifiers, with a reduction of over 20% in the error rate on a challenging marine science application.

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 Dates: 2005-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 5074
 Degree: -

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Title: CVPR 2005 Workshop on Learning in Computer Vision and Pattern Recognition
Place of Event: San Diego, CA, USA
Start-/End Date: 2005-09-21 - 2005-09-23

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Title: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05): Workshops
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Los Alamitos, CA, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1 - 8 Identifier: ISBN: 0-7695-2660-8