Ordinal Measures for Image Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The ANN-tree: An Index for Efficient Approximate Nearest Neighbor Search
DASFAA '01 Proceedings of the 7th International Conference on Database Systems for Advanced Applications
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
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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To improve the accuracy and efficiency of image copy detection, a novel system is proposed based on Graphics Processing Units (GPU). We combine two complementary local features, Harris-Laplace and SURF, to provide a compact representation of an image. By using complementary features, the image is better covered and the detection accuracy becomes less dependent on the actual image content. Moreover, ordinal measure (OM) is applied as semilocal spatial coherent verification. To improve time performance, the process of local features generation and OM calculating are implemented on the GPU through NVIDIA CUDA. Experiments show that our system achieves a 15% precision improvement over the baseline Hamming embedding approach. Compared to the CPU-based method, the GPU realization reaches up to a 30-40x speedup, having real-time performance.