Abstract
Indexing structure plays an important role in the application of fast near-duplicate image detection, since it can narrow down the search space. In this article, we develop a cluster of uniform randomized trees (URTs) as an efficient indexing structure to perform fast near-duplicate image detection. The main contribution in this article is that we introduce “uniformity” and “randomness” into the indexing construction. The uniformity requires classifying the object images into the same scale subsets. Such a decision makes good use of the two facts in near-duplicate image detection, namely: (1) the number of categories is huge; (2) a single category usually contains only a small number of images. Therefore, the uniform distribution is very beneficial to narrow down the search space and does not significantly degrade the detection accuracy. The randomness is embedded into the generation of feature subspace and projection direction, improveing the flexibility of indexing construction. The experimental results show that the proposed method is more efficient than the popular locality-sensitive hashing and more stable and flexible than the traditional KD-tree.
- A. Andoni and P. Indyk. 2008. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Comm. ACM 51, 1, 117--122. Google ScholarDigital Library
- D. N. Bhat and S. K. Nayar. 1998. Ordinal measures for image correspondence. IEEE Trans. Pattern Anal. Mach. Intell. 20, 4, 415--423. Google ScholarDigital Library
- A. Bosch, A. Zisserman, and X. Muoz. 2007. Image classification using random forests and ferns. In Proceedings of the IEEE International Conference on Computer Vision. 1--8.Google Scholar
- Y. Cao, H. Zhang, and J. Guo. 2011. Weakly supervised locality sensitive hashing for duplicate image retrieval. In Proceedings of the IEEE International Conference on Image Processing. 2461--2464.Google Scholar
- E. Chang, J. Wang, C. Li, and G. Wiederhold. 1998. Rime: A replicated image detector for the world-wide web. In SPIE Multimedia Storage and Archiving System III, Vol. 3527, 58--67.Google ScholarCross Ref
- O. Chum, J. Philbin, M. Isard, and A. Zisserman. 2007. Scalable near identical image and shot detection. In Proceedings of the ACM International Conference on Image and Video Retrieval. 549--556. Google ScholarDigital Library
- M. Douze, H. Jegou, H. Sandhawalia, L. Amsaleg, and C. Schmid. 2009. Evaluation of gist descriptors for web-scale image search. In Proceedings of the ACM International Conference on Image and Video Retrieval. Vol. 19. 1--8. Google ScholarDigital Library
- Y. Hu, M. Li, and N. Yu. 2008. Efficient near-duplicate image detection by learning from examples. In Proceedings of the IEEE International Conference on Multimedia and Expo. 657--660.Google Scholar
- M. J. Huiskes and M. S. Lew. 2008. The MIR Flickr retrieval evaluation. In Proceedings of the ACM International Conference on Multimedia Information Retrieval. 39--43. Google ScholarDigital Library
- H. Jegou, F. Perronnin, M. Douze, J. Sanchez, P. Perez, and C. Schmid. 2012. Aggregating local images descriptors into compact codes. IEEE Trans. Pattern Anal. Mach. Intell. 34, 9, 1704--1716. Google ScholarDigital Library
- A. Joly, O. Buisson, and C. Frelicot. 2007. Content-based copy retrieval using distortion-based probabilistic similarity search. IEEE Trans. Multimedia 9, 2, 293--306. Google ScholarDigital Library
- Y. Ke, R. Sukthankar, L. Huston, Y. Ke, and R. Sukthankar. 2004. Efficient near-duplicate detection and sub-image retrieval. In Proceedings of the ACM International Conference on Multimedia. 869--876. Google ScholarDigital Library
- C. Kim. 2003. Content-based image copy detection. Signal Process. Image Comm. 18, 3, 169--184.Google ScholarCross Ref
- Y. Lei, Y. Wang, and J. Huang. 2011. Robust image hash in radon transform domain for authentication. Signal Process. Image Comm. 26, 6, 280--288. Google ScholarDigital Library
- V. Lepetit and P. Fua. 2006. Keypoint recognition using randomized trees. IEEE Trans. Pattern Anal. Mach. Intell. 28, 9, 1465--1479. Google ScholarDigital Library
- D. G. Lowe. 2004. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 2, 91--110. Google ScholarDigital Library
- A. Oliva and A. Torralba. 2001. Modeling the shape of the scene: A holistic representation of the spatial envelope. Int. J. Comput. Vis. 42, 3, 145--175. Google ScholarDigital Library
- A. Qamra, Y. Meng, and E. Y. Chang. 2005. Enhanced perceptual distance functions and indexing for image replica recognition. IEEE Trans. Pattern Anal. Mach. Intell. 27, 3, 379--391. Google ScholarDigital Library
- G. Qiu, J. Morris, and X. Fan. 2007. Visual guided navigation for image retrieval. Pattern Recogn. 40, 6, 1711--1721. Google ScholarDigital Library
- J. Ramirez, J. M. Gorriz, R. Chaves, M. Lopez, D. Salas-Gonzalez, I. Alvarez, and F. Segovia. 2009. SPECT image classification using random forests. Electron. Lett. 45, 12, 604--605.Google ScholarCross Ref
- B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman. 2008. LabelMe: A database and web-based tool for image annotation. Int. J. Comput. Vis. 77, 1--3, 157--173. Google ScholarDigital Library
- G. Shakhnarovich. 2008. The source code of locality sensitive hashing. http://www.ttic.edu/gregory.Google Scholar
- J. Sivic and A. Zisserman. 2003. Video google: A text retrieval approach to object matching in videos. In Proceedings of the IEEE International Conference on Computer Vision. 1470--1477. Google ScholarDigital Library
- M.-N. Wu, C.-C. Lin, and C.-C. Chang. 2007. Novel image copy detection with rotating tolerance. J. Syst. Softw. 80, 7, 1057--1069. Google ScholarDigital Library
- Z. Wu, Q. Ke, M. Isard, and J. Sun. 2009. Bundling features for large scale partial-duplicate web image search. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition. 25--32.Google Scholar
- Z. Wu, Q. Xu, S. Jiang, Q. Huang, P. Cui, and L. Li. 2010. Adding affine invariant geometric constraint for partial-duplicate image retrieval. In Proceedings of the IEEE International Conference on Pattern Recognition. 842--845. Google ScholarDigital Library
- Z. Xu, H. Ling, F. Zou, Z. Lu, and P. Li. 2010. Robust image copy detection using multi-resolution histogram. In Proceedings of the ACM International Conference on Multimedia Information Retrieval. 129--136. Google ScholarDigital Library
- G. Yu, N. A. Goussies, J. Yuan, and Z. Liu. 2011. Fast action detection via discriminative random forest voting and top-k subvolume search. IEEE Trans. Multimedia 13, 3, 507--517. Google ScholarDigital Library
- L. Zheng, Y. Lei, G. Qiu, and J. Huang. 2012. Near-duplicate image detection in a visually salient riemannian space. IEEE Trans. Inf. Forens. Secur. 7, 5, 1578--1593. Google ScholarDigital Library
Index Terms
- Fast Near-Duplicate Image Detection Using Uniform Randomized Trees
Recommendations
Near-Duplicate Image Detection in a Visually Salient Riemannian Space
This paper presents a framework for near-duplicate image detection in a visually salient Riemannian space. A visual saliency model is first used to identify salient regions of the image and then the salient region covariance matrix (SCOV) of various ...
Using redundant bit vectors for near-duplicate image detection
DASFAA'07: Proceedings of the 12th international conference on Database systems for advanced applicationsImages are amongst the most widely proliferated form of digital information due to affordable imaging technologies and the Web. In such an environment, the use of digital watermarking for image copyright infringement detection is a challenge. For such ...
Speed up duplicate/near-duplicate image detection
ICIMCS '10: Proceedings of the Second International Conference on Internet Multimedia Computing and ServiceFinding duplicate and near-duplicate images plays an important role on redundancy reduction for image storage, summarization and recommendation. This paper introduces how to speed up Duplicate/Near-Duplicate(D/ND) image detection. Image clustering was ...
Comments