@inproceedings{79e4bba8651b4989acb233a631250483,
title = "Image set classification using holistic multiple order statistics features and localized multi-kernel metric learning",
abstract = "This paper presents a new approach for image set classification, where each training and testing example contains a set of image instances of an object captured from varying viewpoints or under varying illuminations. While a number of image set classification methods have been proposed in recent years, most of them model each image set as a single linear subspace or mixture of linear subspaces, which may lose some discriminative information for classification. To address this, we propose exploring multiple order statistics as features of image sets, and develop a localized multi-kernel metric learning (LMKML) algorithm to effectively combine different order statistics information for classification. Our method achieves the state-of-the-art performance on four widely used databases including the Honda/UCSD, CMU Mobo, and Youtube face datasets, and the ETH-80 object dataset.",
keywords = "Image set classification, face recognition, metric learning, object recognition",
author = "Jiwen Lu and Gang Wang and Pierre Moulin",
year = "2013",
doi = "10.1109/ICCV.2013.48",
language = "English (US)",
isbn = "9781479928392",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "329--336",
booktitle = "Proceedings - 2013 IEEE International Conference on Computer Vision, ICCV 2013",
address = "United States",
note = "2013 14th IEEE International Conference on Computer Vision, ICCV 2013 ; Conference date: 01-12-2013 Through 08-12-2013",
}