TY - GEN
T1 - Coherent regions for concise and stable image description
AU - Corso, Jason J.
AU - Hager, Gregory D.
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=24644501836&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=24644501836&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2005.100
DO - 10.1109/CVPR.2005.100
M3 - Conference contribution
AN - SCOPUS:24644501836
SN - 0769523722
SN - 9780769523729
T3 - Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
SP - 184
EP - 190
BT - Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
T2 - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
Y2 - 20 June 2005 through 25 June 2005
ER -