Heng Yang
ABSTRACT
High-dimensional image feature matching is an important part of many image matching based problems in computer vision which are solved by local invariant features. In this paper, we propose a new indexing/searching method based on Randomized Sub-Vectors Hashing (called RSVH) for high-dimensional image feature matching. The essential of the proposed idea is that the feature vectors are considered similar (measured by Euclidean distance) when the L2 norms of their corresponding randomized sub-vectors are approximately same respectively. Experimental results have demonstrated that our algorithm can perform much better than the famous BBF (Best-Bin-First) and LSH (Locality Sensitive Hashing) algorithms in extensive image matching and image retrieval applications.
- Noah Snavely, Steven M. Seitz, Richard Szeliski. 2006 Photo tourism: Exploring photo collections in 3D. ACM Transactions on Graphics, 25(3):835--846. Google Scholar
Digital Library
- M. Brown and D. G. Lowe. 2007 Automatic Panoramic Image Stitching Using Invariant Features. IJCV, 74(1):59--73. Google ScholarDigital Library
- K. Mikolajczyk, B. Leibe and B. Schiele. 2005 Local feature for object class recognition. In ICCV, pages 1792--1799. Google ScholarDigital Library
- J. Yao and W. K. Cham. 2007 Robust multi-view feature matching from multiple unordered views. Pattern Recognition, 40:3081--3099. Google ScholarDigital Library
- D. G. Lowe. 2004 Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91--110. Google ScholarDigital Library
- K. Mikolajczyk and C. Schmid. 2005 A Performance Evaluation of Local Descriptors, IEEE PAMI, 27 (10):1615--1630. Google ScholarDigital Library
- J. H. Firedman, J. L. Bentley, R. A. Finkel. 1977 An algorithm for finding best matches in logarithmic expected time. ACM Transactions Mathematical Software (3)3 pp.209--226. Google ScholarDigital Library
- Sameer A. Nene , Shree K. Nayar. 1997 A Simple Algorithm for Nearest Neighbor Search in High Dimensions. IEEE Transactions on pattern analysis and machine intelligence, (19) 9, pp.989--1003. Google ScholarDigital Library
- C. Yu, B. C. Ooi, K. L. Tan, H. V. Jgadish. 2001 Indexing the Distance: An Efficient Method to KNN Processing. Proceedings of the 27th VLDB Conference, pp. 421--430. Google ScholarDigital Library
- Aristides Gionis, Piotr Indyky and Rajeev Motwaniz. 1999 Similarity Search in High Dimensions via Hashing. In The VLDB Journal, pp. 518--529. Google ScholarDigital Library
- Test images for image matching. http://lear.inrialpes.fr/people/mikolajczyk/Google Scholar
- Image retrieval dataset. http://vis.uky.edu/~stewe/ukbench/Google Scholar
Index Terms
- Randomized sub-vectors hashing for high-dimensional image feature matching
Recommendations
Grassmann Hashing for approximate nearest neighbor search in high dimensional space
ICME '11: Proceedings of the 2011 IEEE International Conference on Multimedia and ExpoLocality-Sensitive Hashing (LSH) approximates nearest neighbors in high dimensions by projecting original data into low-dimensional subspaces. The basic idea is to hash data samples to ensure that the probability of collision is much higher for samples ...
Constrained discriminant neighborhood embedding for high dimensional data feature extraction
When handling pattern classification problem such as face recognition and digital handwriting identification, image data is always represented to high dimensional vectors, from which discriminant features are extracted using dimensionality reduction ...
High-dimensional image data feature extraction by double discriminant embedding
We propose a supervised feature extraction method in this paper that uses two successive transformations to produce the extracted features. The first projection maximizes the difference between spectral features. Thus, produced features have minimum ...
Comments