Multi-view anchor graph hashing
- 1 May 2013
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 3123-3127
- https://doi.org/10.1109/icassp.2013.6638233
Abstract
Multi-view hashing seeks compact integrated binary codes which preserve similarities averaged over multiple representations of objects. Most of existing multi-view hashing methods resort to linear hash functions where data manifold is not considered. In this paper we present multi-view anchor graph hashing (MVAGH), where nonlinear integrated binary codes are efficiently determined by a subset of eigenvectors of an averaged similarity matrix. The efficiency behind MVAGH is due to a low-rank form of the averaged similarity matrix induced by multi-view anchor graph, where the similarity between two points is measured by two-step transition probability through view-specific anchor (i.e. landmark) points. In addition, we observe that MVAGH suffers from the performance degradation when the high recall is required. To overcome this drawback, we propose a simple heuristic to combine MVAGH with locality sensitive hashing (LSH). Numerical experiments on CIFAR-10 dataset confirms that MVAGH(+LSH) outperforms the existing multi- and single-view hashing methods.Keywords
This publication has 8 references indexed in Scilit:
- A probabilistic model for multimodal hash function learningPublished by Association for Computing Machinery (ACM) ,2012
- Composite hashing with multiple information sourcesPublished by Association for Computing Machinery (ACM) ,2011
- Iterative quantization: A procrustean approach to learning binary codesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Semi-supervised hashing for scalable image retrievalPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- Small codes and large image databases for recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- Histograms of Oriented Gradients for Human DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Multiclass spectral clusteringPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Modeling the Shape of the Scene: A Holistic Representation of the Spatial EnvelopeInternational Journal of Computer Vision, 2001