Discriminative Embedding via Image-to-Class Distances

Zhen, 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.

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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
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Depositing User: Pure Connector
Date Deposited: 10 Feb 2017 02:29
Last Modified: 22 Apr 2020 09:37
URI: https://ueaeprints.uea.ac.uk/id/eprint/62415
DOI:

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