A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Image Corner Detection Through Curvature Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Multimedia database management systems
Multimedia database management systems
Chord-to-point distance accumulation and planar curvature: a new approach to discrete curvature
Pattern Recognition Letters
Comparing and Evaluating Interest Points
ICCV '98 Proceedings of the Sixth International Conference on 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
Curvature Scale Space Corner Detector with Adaptive Threshold and Dynamic Region of Support
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
Detecting image near-duplicate by stochastic attributed relational graph matching with learning
Proceedings of the 12th annual ACM international conference on Multimedia
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Multi-scale curvature product for robust image corner detection in curvature scale space
Pattern Recognition Letters
A model-based approach to junction detection using radial energy
Pattern Recognition
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
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Near-Duplicate Keyframe Identification With Interest Point Matching and Pattern Learning
IEEE Transactions on Multimedia
Robust Image Corner Detection Based on the Chord-to-Point Distance Accumulation Technique
IEEE Transactions on Multimedia
Enhanced independent component analysis and its application to content based face image retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multitraining Support Vector Machine for Image Retrieval
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Human Gait Recognition With Matrix Representation
IEEE Transactions on Circuits and Systems for Video Technology
Which Components are Important for Interactive Image Searching?
IEEE Transactions on Circuits and Systems for Video Technology
An effective method of estimating scale-invariant interest region for representing corner features
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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In many applications, one common problem is to identify images which may have undergone unknown transformations. We define this problem as transformed image identification (TII), where the goal is to identify geometrically transformed and signal processed images for a given test image. The TII consists of three main stages -feature detection, feature representation, and feature matching. The TII approach by Lowe [D.G. Lowe, Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vision 60 (2) (2004) 91-110] is one of the most promising techniques. However, both of its feature detection and matching stages are expensive, because a large number of feature-points are detected in the image scale-space and each feature-point is described using a high dimensional vector. In this paper, we explore the use of different techniques in each of the three TII stages and propose a number of promising TII approaches by combining different techniques of the three stages. Our experimental results reveal that the proposed approaches not only improve the computational efficiency and decrease the storage requirement significantly, but also increase the transformed image identification accuracy (robustness).