Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
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
Robust content-based image searches for copyright protection
MMDB '03 Proceedings of the 1st ACM international workshop on Multimedia databases
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
An efficient parts-based near-duplicate and sub-image retrieval system
Proceedings of the 12th annual ACM international conference on Multimedia
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effective and efficient object-based image retrieval using visual phrases
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Pruning SIFT for scalable near-duplicate image matching
ADC '07 Proceedings of the eighteenth conference on Australasian database - Volume 63
Seam carving for content-aware image resizing
ACM SIGGRAPH 2007 papers
Evaluating bag-of-visual-words representations in scene classification
Proceedings of the international workshop on Workshop on multimedia information retrieval
Multi-probe LSH: efficient indexing for high-dimensional similarity search
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Language modeling for bag-of-visual words image categorization
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Scalable mining of large video databases using copy detection
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Self-taught hashing for fast similarity search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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
Flexible spatial models for grouping local image features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Content-Based Copy Retrieval Using Distortion-Based Probabilistic Similarity Search
IEEE Transactions on Multimedia
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
| Hi-index | 0.08 |
Recently the methods based on bag-of-visual words have become very popular in near-duplicate retrieval and content identification. However, obtaining the visual vocabulary by quantization is very time-consuming and unscalable to large databases. In this paper, we propose a fast copy detection method which uses local image fingerprints to define visual words. To construct the fingerprint, a 32-bit vector is extracted from the local description and then converted into a number which is used to define the visual word. Then, a histogram intersection is employed to measure the similarity between two images. Since the fingerprint building is of low-complexity, this method is very efficient and scalable to very large databases. Furthermore, the fingerprint-defined visual words are more discriminative and precise than the clustering-defined visual words because the vocabulary size could be large enough while maintaining high efficiency. Visual words with strong discriminability can distinguish copies from similar objects, which can reduce the number of false positives and improve the precision and efficiency. The evaluation shows that our approach significantly outperforms state-of-the-art methods.