Improving classification accuracy by comparing local features through canonical correlations

Mert Dikmen, Thomas S Huang

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

Abstract

Classifying images using features extracted from densely sampled local patches has enjoyed significant success in many detection and recognition tasks. It is also well known that generally more than one type of feature is needed to achieve robust classification performance. Previous works using multiple features have addressed this issue either through simple concatenation of feature vectors or through combining feature specific kernels at the classifier level. In this work we introduce a novel approach for combining features at the feature level by projecting two types of features onto two respective subspaces in which they are maximally correlated. We use their correlation as an augmented feature and demonstrate improvement in classification accuracy over simple combination through concatenation in a pedestrian detection framework.

Original languageEnglish (US)
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Pages4032-4035
Number of pages4
DOIs
StatePublished - Nov 18 2010
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: Aug 23 2010Aug 26 2010

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2010 20th International Conference on Pattern Recognition, ICPR 2010
CountryTurkey
CityIstanbul
Period8/23/108/26/10

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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