SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hyperplane Approximation for Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Statistical Approach to Texture Classification from Single Images
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Fusing Points and Lines for High Performance Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Comparison of Affine Region Detectors
International Journal of Computer Vision
International Journal of Computer Vision
Keypoint Recognition Using Randomized Trees
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
Visual Tracking Algorithm Using Pixel-Pair Feature
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Multilayer Ferns: A Learning-based Approach of Patch Recognition and Homography Extraction
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Region covariance: a fast descriptor for detection and classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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Nowadays, with the help of photo processing software, it is easy to `create' a photo from other people's photography works. So, more and more unoriginal photography works have appeared in some photography contests. In order to protect the copyright and maintain fairness, it is crucial to recognize these plagiarisms. However, it is a difficult task because most plagiarisms have undergone copying, scaling, cropping and other processing. Even worse, most original copies don't have any digital watermarks on them.In this paper, we propose a novel learning-based feature matching approach to deal with this problem. It uses affine invariant features to identity bogus photos. First, we adopt an extremely fast algorithm to extract keypoints. Then, using color and texture representation, the keypoints that belong to different objects or background are clustered into corresponding groups. Next, based on the partition of the deformation space, a multilayer ferns model is trained to recognize local patches and get coarse pose estimations at the same time. At last, a linear predictor is adopted to refine the estimation, so as to get the accurate homography. We test our approach on several public datasets and a special dataset from national photography database. The experiment result demonstrates that our method can provide robust and powerful matching ability. Especially in some difficult matching conditions, in which other state-of-the-art methods can not yield good result, our approach also performs remarkably well. Furthermore, as there is no need to compute complicated descriptors, our method is very fast at run-time.