Title :
Image Set Classification Using Holistic Multiple Order Statistics Features and Localized Multi-kernel Metric Learning
Author :
Jiwen Lu ; Gang Wang ; Moulin, Philippe
Author_Institution :
Adv. Digital Sci. Center, Singapore, Singapore
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 classification; learning (artificial intelligence); statistical analysis; CMU Mobo face datasets; ETH-80 object dataset; Honda UCSD face datasets; LMKML algorithm; Youtube face datasets; holistic multiple order statistics features; image set classification method; localized multikernel metric learning algorithm; single linear subspace; Face; Feature extraction; Kernel; Measurement; Training; Vectors; YouTube; Image set classification; face recognition; metric learning; object recognition;
Conference_Titel :
Computer Vision (ICCV), 2013 IEEE International Conference on
Conference_Location :
Sydney, NSW
DOI :
10.1109/ICCV.2013.48