Local ensemble kernel learning for object category recognition

Yen-Yu Lin*, Tyng Luh Liu, Chiou Shann Fuh

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

72 Scopus citations

Abstract

This paper describes a local ensemble kernel learning technique to recognize/classify objects from a large number of diverse categories. Due to the possibly large intraclass feature variations, using only a single unified kernel-based classifier may not satisfactorily solve the problem. Our approach is to carry out the recognition task with adaptive ensemble kernel machines, each of which is derived from proper localization and regularization. Specifically, for each training sample, we learn a distinct ensemble kernel constructed in a way to give good classification performance for data falling within the corresponding neighborhood. We achieve this effect by aligning each ensemble kernel with a locally adapted target kernel, followed by smoothing out the discrepancies among kernels of nearby data. Our experimental results on various image databases manifest that the technique to optimize local ensemble kernels is effective and consistent for object recognition.

Original languageEnglish
Title of host publication2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
DOIs
StatePublished - 12 Oct 2007
Event2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States
Duration: 17 Jun 200722 Jun 2007

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

Conference2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
CountryUnited States
CityMinneapolis, MN
Period17/06/0722/06/07

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    Lin, Y-Y., Liu, T. L., & Fuh, C. S. (2007). Local ensemble kernel learning for object category recognition. In 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 [4270109] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2007.383084