An expectation maximization approach to the synergy between image segmentation and object categorization

Kokkinos, I; Maragos, P

dc.contributor.author	Kokkinos, I	en
dc.contributor.author	Maragos, P	en
dc.date.accessioned	2014-03-01T02:43:06Z
dc.date.available	2014-03-01T02:43:06Z
dc.date.issued	2005	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31236
dc.subject	Em Algorithm	en
dc.subject	Expectation Maximization	en
dc.subject	Generic Model	en
dc.subject	Image Segmentation	en
dc.subject	Object Categorization	en
dc.subject	Object Detection	en
dc.subject.other	Adaptive algorithms	en
dc.subject.other	Computer simulation	en
dc.subject.other	Computer vision	en
dc.subject.other	Image segmentation	en
dc.subject.other	Optimization	en
dc.subject.other	Problem solving	en
dc.subject.other	Expectation Maximization (EM) algorithms	en
dc.subject.other	Fitting models	en
dc.subject.other	Generative models	en
dc.subject.other	Object categorization	en
dc.subject.other	Object recognition	en
dc.title	An expectation maximization approach to the synergy between image segmentation and object categorization	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICCV.2005.35	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICCV.2005.35	en
heal.identifier.secondary	1541311	en
heal.publicationDate	2005	en
heal.abstract	In this work we deal with the problem of modelling and exploiting the interaction between the processes of image segmentation and object categorization. We propose a novel framework to address this problem that is based on the combination of the Expectation Maximization (EM) algorithm and generative models for object categories. Using a concise formulation of the interaction between these two processes, segmentation is interpreted as the E step, assigning observations to models, whereas object detection/analysis is modelled as the M-step, fitting models to observations. We present in detail the segmentation and detection processes comprising the E and M steps and demonstrate results on the joint detection and segmentation of the object categories of faces and cars. © 2005 IEEE.	en
heal.journalName	Proceedings of the IEEE International Conference on Computer Vision	en
dc.identifier.doi	10.1109/ICCV.2005.35	en
dc.identifier.volume	I	en
dc.identifier.spage	617	en
dc.identifier.epage	624	en