Effective classifiers for detecting objects

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dc.contributor.authorMayo, Michael
dc.coverage.spatialConference held at Palmerston North, New Zealanden_NZ
dc.date.accessioned2009-05-20T23:47:00Z
dc.date.available2009-05-20T23:47:00Z
dc.date.issued2007
dc.description.abstractSeveral state-of-the-art machine learning classifiers are compared for the purposes of object detection in complex images, using global image features derived from the Ohta color space and Local Binary Patterns. Image complexity in this sense refers to the degree to which the target objects are occluded and/or non-dominant (i.e. not in the foreground) in the image, and also the degree to which the images are cluttered with non-target objects. The results indicate that a voting ensemble of Support Vector Machines, Random Forests, and Boosted Decision Trees provide the best performance with AUC values of up to 0.92 and Equal Error Rate accuracies of up to 85.7% in stratified 10-fold cross validation experiments on the GRAZ02 complex image dataset.en
dc.format.mimetypeapplication/pdf
dc.identifier.citationMayo M. (2007). Effective classifiers for detecting objects. In Proceedings of the Fourth International Conference on Computational Intelligence, Robotics, and Autonomous Systems (CIRAS ’07), Palmerston North, New Zealand.en
dc.identifier.urihttps://hdl.handle.net/10289/2171
dc.language.isoen
dc.publisherMassey Universityen_NZ
dc.relation.isPartOfProc 4th International Conference on Computational Intelligence, Robotics and Autonomous Systemsen_NZ
dc.relation.urihttp://www-ist.massey.ac.nz/conferences/ciras/schedule.aspen
dc.subjectcomputer scienceen
dc.subjectimage classificationen
dc.subjectrandom convolutionen
dc.subjectpedestrian detectionen
dc.subjectMachine learning
dc.titleEffective classifiers for detecting objectsen
dc.typeConference Contributionen
pubs.begin-page184en_NZ
pubs.elements-id17704
pubs.end-page189en_NZ
pubs.finish-date2007-11-30en_NZ
pubs.start-date2007-11-28en_NZ
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