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ToolsZhen, Xiantong, Shao, Ling and Zheng, Feng (2014) Discriminative Embedding via Image-to-Class Distances. In: 25th British Machine Vision Conference, BMVC 2014, 2014-09-01 - 2014-09-05.
Full text not available from this repository.Abstract
Image-to-Class (I2C) distance firstly proposed in the naive Bayes nearest neighbour (NBNN) classifier has shown its effectiveness in image classification. However, due to the large number of nearest-neighbour search, I2C-based methods are extremely time-consuming, especially with highdimensional local features. In this paper, with the aim to improve and speed up I2C-based methods, we propose a novel discriminative embedding method based on I2C for local feature dimensionality reduction. Our method 1) greatly reduces the computational burden and improves the performance of I2C-based methods after reduction; 2) can well preserve the discriminative ability of local features, thanks to the use of I2C distances; and 3) provides an efficient closed-form solution by formulating the objective function as an eigenvector decomposition problem. We apply the proposed method to action recognition showing that it can significantly improve I2C-based classifiers.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| Related URLs: | |
| Depositing User: | Pure Connector |
| Date Deposited: | 10 Feb 2017 02:29 |
| Last Modified: | 21 Oct 2022 23:42 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/62415 |
| DOI: |
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