Asymmetric region-to-image matching for comparing images with generic object categories

We present a feature matching algorithm that leverages bottom-up segmentation. Unlike conventional image-to-image or region-to-region matching algorithms, our method finds corresponding points in an "asymmetric" manner, matching features within each region of a segmented image to a second...

Full description

Saved in:
Bibliographic Details
Published in2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition pp. 2344 - 2351
Main Authors Jaechul Kim, Grauman, K
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
Computer vision
Distortion measurement
Dynamic programming
Encoding
Image matching
Image recognition
Image retrieval
Image segmentation
Object recognition
Testing
Online AccessGet full text

Cover

More Information
Summary:We present a feature matching algorithm that leverages bottom-up segmentation. Unlike conventional image-to-image or region-to-region matching algorithms, our method finds corresponding points in an "asymmetric" manner, matching features within each region of a segmented image to a second unsegmented image. We develop a dynamic programming solution to efficiently identify corresponding points for each region, so as to maximize both geometric consistency and appearance similarity. The final matching score between two images is determined by the union of corresponding points obtained from each region-to-image match. Our encoding for the geometric constraints makes the algorithm flexible when matching objects exhibiting non-rigid deformations or intra-class appearance variation. We demonstrate our image matching approach applied to object category recognition, and show on the Caltech-256 and 101 datasets that it outperforms existing image matching measures by 10~20% in nearest-neighbor recognition tests.
ISBN:1424469848
9781424469840
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2010.5539923