Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Unbiased Detector of Curvilinear Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Local Transforms for Computing Visual Correspondence
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Image Indexing using Composite Color and Shape Invariant Features
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Selection of Scale-Invariant Parts for Object Class Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
A SIFT Descriptor with Global Context
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Comparison of Affine Region Detectors
International Journal of Computer Vision
CSIFT: A SIFT Descriptor with Color Invariant Characteristics
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Selection and Fusion of Color Models for Image Feature Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining color and shape information for illumination-viewpoint invariant object recognition
IEEE Transactions on Image Processing
Three-dimensional facial feature points matching based on a combined support vector machine
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Document retrieval using image features
Proceedings of the 2010 ACM Symposium on Applied Computing
MIFT: A framework for feature descriptors to be mirror reflection invariant
Image and Vision Computing
An improvement to the SIFT descriptor for image representation and matching
Pattern Recognition Letters
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The description of interest points is a critical aspect of point correspondence which is vital in some computer vision and pattern recognition tasks. SIFT descriptor has been proven to perform better on the distinctiveness and robustness than other local descriptors. But SIFT descriptor does not involve color and global information of feature point which provides powerfully distinguishable signals in feature description and matching tasks, so many mismatches may occur. This paper improves SIFT descriptor, and presents a new framework for feature descriptor based on SIFT by integrating color and global information with it. The proposed framework consists of the improved SIFT, color invariance components and global component. We use a log-polar histogram to build three color invariance components and the global component of the proposed framework, respectively. In addition, the elliptical neighboring region for every interest point is used so as to make the framework fully invariant to common affine transformations. Experimental comparison with three related feature descriptors is carried out in two groups of experiments, validating the proposed framework.