FIM: A Real-Time Content Based Sample Image Matching System

Sample Image Matching is to decide if a queried image is belongs to the database or not. In this paper, we focus on real-time image matching, which is critical in many real world applications. Although traditional image retrieval methods can be directly utilized for image matching, they usually suffer the high computational cost problem and thus is not applicable here. To resolve this problem, we first introduce ORB, a recently proposed and well-established image feature, for image matching. Then we compare several variants of the descriptors, different size of the codebook, and two approaches to compute the matching scores, based on which we propose a strategy for final matching decision. According to the comparison results, we finally present a real-time image matching system, fast image matching (FIM), which can process about 33 images per second, with a satisfactory accuracy.

[1]  Andrzej Sluzek,et al.  Beyond Keypoints: Novel Techniques for Content-Based Image Matching and Retrieval , 2010, ICAISC.

[2]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[3]  Bo Geng,et al.  Manifold Regularized Multi-task Learning for Semi-supervised Multi-label Image Classification , 2013 .

[4]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[5]  Yong Luo,et al.  Multiview Vector-Valued Manifold Regularization for Multilabel Image Classification , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[7]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[8]  Noah Snavely,et al.  Image matching using local symmetry features , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  David G. Lowe,et al.  Shape indexing using approximate nearest-neighbour search in high-dimensional spaces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Haibin Ling,et al.  Deformation invariant image matching , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[11]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[12]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[13]  Richard I. Hartley,et al.  Optimised KD-trees for fast image descriptor matching , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Michael Isard,et al.  Object retrieval with large vocabularies and fast spatial matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Michael Isard,et al.  Lost in quantization: Improving particular object retrieval in large scale image databases , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Zheng-Jun Zha,et al.  Difficulty Guided Image Retrieval Using Linear Multiple Feature Embedding , 2012, IEEE Transactions on Multimedia.

[17]  Yong Luo,et al.  Manifold Regularized Multitask Learning for Semi-Supervised Multilabel Image Classification , 2013, IEEE Transactions on Image Processing.

[18]  David G. Lowe,et al.  Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration , 2009, VISAPP.

[19]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[20]  Paul L. Rosin.  Measuring Corner Properties , 1999, Comput. Vis. Image Underst..

[21]  Arnold W. M. Smeulders,et al.  Real-time bag of words, approximately , 2009, CIVR '09.