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BMVC
2010
13 years 2 months ago
Evaluation of dimensionality reduction methods for image auto-annotation
Image auto-annotation is a challenging task in computer vision. The goal of this task is to predict multiple words for generic images automatically. Recent state-of-theart methods...
Hideki Nakayama, Tatsuya Harada, Yasuo Kuniyoshi
PAMI
2011
12 years 11 months ago
Learning Linear Discriminant Projections for Dimensionality Reduction of Image Descriptors
This paper proposes a general method for improving image descriptors using discriminant projections. Two methods based on Linear Discriminant Analysis have been recently introduce...
Hongping Cai, Krystian Mikolajczyk, Jiri Matas
WACV
2002
IEEE
13 years 9 months ago
An Experimental Evaluation of Linear and Kernel-Based Methods for Face Recognition
In this paper we present the results of a comparative study of linear and kernel-based methods for face recognition. The methods used for dimensionality reduction are Principal Co...
Himaanshu Gupta, Amit K. Agrawal, Tarun Pruthi, Ch...
ICIP
2005
IEEE
14 years 6 months ago
Nonlinear dimensionality reduction for classification using kernel weighted subspace method
We study the use of kernel subspace methods that learn low-dimensional subspace representations for classification tasks. In particular, we propose a new method called kernel weigh...
Guang Dai, Dit-Yan Yeung
ICIP
2006
IEEE
14 years 6 months ago
Wavelet Principal Component Analysis and its Application to Hyperspectral Images
We investigate reducing the dimensionality of image sets by using principal component analysis on wavelet coefficients to maximize edge energy in the reduced dimension images. Lar...
Maya R. Gupta, Nathaniel P. Jacobson