Coherent regions for concise and stable image description

Jason J. Corso, Gregory D. Hager

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

We present a new method for summarizing images for the purposes of matching and registration. We take the point of view that large, coherent regions in the image provide a concise and stable basis for image description. We develop a new algorithm for image segmentation that operates on several projections (feature spaces) of the image, using kernel-based optimization techniques to locate local extrema of a continuous scale-space of image regions. Descriptors of these image regions and their relative geometry then form the basis of an image description. We present experimental results of these methods applied to the problem of image retrieval On a moderate sized database, we find that our method performs comparably to two published techniques: Blobworld and SIFT features. However, compared to these techniques two significant advantages of our method are its 1) stability under large changes in the images and 2) its representational efficiency. As a result we argue our proposed method will scale well with larger image sets.

Original languageEnglish
Title of host publicationProceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
Pages184-190
Number of pages7
DOIs
StatePublished - 2005
Event2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - San Diego, CA, United States
Duration: 20 Jun 200525 Jun 2005

Publication series

NameProceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
VolumeII

Conference

Conference2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
Country/TerritoryUnited States
CitySan Diego, CA
Period20/06/0525/06/05

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