Toward A Discriminative Codebook: Codeword Selection across Multi-resolution

dc.contributor.authorWang, Lei
dc.coverage.spatialMinneapolis USA
dc.date.accessioned2015-12-10T21:54:27Z
dc.date.createdJune 18-23 2007
dc.date.issued2007
dc.date.updated2015-12-09T07:26:26Z
dc.description.abstractIn patch-based object recognition, there are two important issues on the codebook generation: (1) resolution: a coarse codebook lacks sufficient discriminative power, and an over-fine one is sensitive to noise; (2) codeword selection: non-discriminative codewords not only increase the codebook size, but also can hurt the recognition performance. To achieve a discriminative codebook for better recognition, this paper argues that these two issues are strongly related and should be solved as a whole. In this paper, a multi-resolution codebook is first designed via hierarchical clustering. With a reasonable size, it includes all of the codewords which cross a large number of resolution levels. More importantly, it forms a diverse candidate codeword set that is critical to codeword selection. A Boosting feature selection approach is modified to select the discriminative codewords from this multi-resolution codebook. By doing so, the obtained codebook is composed of the most discriminative codewords culled from different levels of resolution. Experimental study demonstrates the better recognition performance attained by this codebook.
dc.identifier.isbn1424411807
dc.identifier.urihttp://hdl.handle.net/1885/38947
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.relation.ispartofseriesComputer Vision and Pattern Recognition Conference (CVPR 2007)
dc.sourceProceedings of the Computer Vision and Pattern Recognition Conference (CVPR 2007)
dc.source.urihttp://cvpr.cv.ri.cmu.edu/
dc.subjectKeywords: Clustering algorithms; Discriminant analysis; Feature extraction; Optical resolving power; Codebook generation; Codebook size; Object recognition
dc.titleToward A Discriminative Codebook: Codeword Selection across Multi-resolution
dc.typeConference paper
local.bibliographicCitation.lastpage8
local.bibliographicCitation.startpage1
local.contributor.affiliationWang, Lei, College of Engineering and Computer Science, ANU
local.contributor.authoremailrepository.admin@anu.edu.au
local.contributor.authoruidWang, Lei, u4259382
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080104 - Computer Vision
local.identifier.ariespublicationu3357961xPUB169
local.identifier.doi10.1109/CVPR.2007.383374
local.identifier.scopusID2-s2.0-34948824863
local.identifier.uidSubmittedByu3357961
local.type.statusPublished Version

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