IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comprehensive Colour Image Normalization
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Color Illumination Models for Image Matching and Indexing
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Moment invariants for recognition under changing viewpoint and illumination
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
The Amsterdam Library of Object Images
International Journal of Computer Vision
Edge and Corner Detection by Photometric Quasi-Invariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining color and spatial information for object recognition across illumination changes
Pattern Recognition Letters
SIFT Features Tracking for Video Stabilization
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Performance evaluation of local colour invariants
Computer Vision and Image Understanding
Object tracking using SIFT features and mean shift
Computer Vision and Image Understanding
ASIFT: A New Framework for Fully Affine Invariant Image Comparison
SIAM Journal on Imaging Sciences
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Coloring local feature extraction
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
A computational model for color naming and describing color composition of images
IEEE Transactions on Image Processing
Robust photometric invariant features from the color tensor
IEEE Transactions on Image Processing
Discriminative feature fusion for image classification
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Hi-index | 0.01 |
Accurate local region description is a keypoint in many applications and has been the topic of lots of recent papers. Starting from the very accurate SIFT, most of the approaches exploit the local gradient information that suffers from several drawbacks. First it is unstable in case of severe geometry distortions, second it cannot be easily summarized in a compact way and third it is not designed to account vectorial color information. In this paper, we propose an alternative by designing compact descriptors that account both the colors present in the region and their spatial distribution. Each pixel being characterized by five coordinates, two in the image space and three in the color space, we try to evaluate affine transforms that allow to go from the spatial coordinates to the color coordinates and inversely. Obviously such kind of transform does not exist but we show that after applying it to the original coordinates, the resulted positions are both discriminative and invariant to many acquisition conditions. Hence, depending on the original space (image or color) and the destination space (color or image), we design different complementary descriptors. Their discriminative power and invariance properties are assessed and compared with the best color descriptors in the context of region matching and object classification.