A Color-Based Interest Operator

Authors:
Marta Penas;Linda G. Shapiro
Affiliations:
Dpt. of Computer Science, University of A Coruña, A Coruña, Spain 15071;Dpt. of Computer Science and Engineering, University of Washington, US
Venue:
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Year:
2009

Citing 10
Cited 0

Geometry from visual motion

Active vision
Local Grayvalue Invariants for Image Retrieval

IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval Based on Local Affinely Invariant Regions

VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
Video Google: A Text Retrieval Approach to Object Matching in Videos

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Automated location matching in movies

Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Matching Widely Separated Views Based on Affine Invariant Regions

International Journal of Computer Vision
Scale & Affine Invariant Interest Point Detectors

International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Coloring local feature extraction

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we propose a novel interest operator robust to photometric and geometric transformations. Our operator is closely related to the grayscale MSER but it works on the HSV color space, as opposed to the most popular operators in the literature, which are intensity based. It combines a fine and a coarse overlapped quantization of the HSV color space to find maximally stable extremal regions on each of its components and combine them into a final set of regions that are useful in images where intensity does not discriminate well. We evaluate the performance of our operator on two different applications: wide-baseline stereo matching and image annotation.