Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/39632
DC FieldValue
dc.titleThe analysis and applications of adaptive-binning color histograms
dc.contributor.authorLeow, W.K.
dc.contributor.authorLi, R.
dc.date.accessioned2013-07-04T07:46:01Z
dc.date.available2013-07-04T07:46:01Z
dc.date.issued2004
dc.identifier.citationLeow, W.K.,Li, R. (2004). The analysis and applications of adaptive-binning color histograms. Computer Vision and Image Understanding 94 (1-3) : 67-91. ScholarBank@NUS Repository.
dc.identifier.issn10773142
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39632
dc.description.abstractHistograms are commonly used in content-based image retrieval systems to represent the distributions of colors in images. It is a common understanding that histograms that adapt to images can represent their color distributions more efficiently than do histograms with fixed binnings. However, existing systems almost exclusively adopt fixed-binning histograms because, among existing well-known dissimilarity measures, only the computationally expensive Earth Mover's Distance (EMD) can compare histograms with different binnings. This paper addresses the issue by defining a new dissimilarity measure that is more reliable than the Euclidean distance and yet computationally less expensive than EMD. Moreover, a mathematically sound definition of mean histogram can be defined for histogram clustering applications. Extensive test results show that adaptive histograms produce the best overall performance, in terms of good accuracy, small number of bins, no empty bin, and efficient computation, compared to existing methods for histogram retrieval, classification, and clustering tasks. © 2003 Elsevier Inc. All rights reserved.
dc.sourceScopus
dc.subjectAdaptive binning
dc.subjectColor histograms
dc.subjectHistogram-based dissimilarity measures
dc.subjectImage classification
dc.subjectImage clustering
dc.subjectImage retrieval
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleComputer Vision and Image Understanding
dc.description.volume94
dc.description.issue1-3
dc.description.page67-91
dc.description.codenCVIUF
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.