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» Image Classification Using Marginalized Kernels for Graphs
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ICIP
2005
IEEE
14 years 9 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
CVPR
2008
IEEE
14 years 9 months ago
Max Margin AND/OR Graph learning for parsing the human body
We present a novel structure learning method, Max Margin AND/OR Graph (MM-AOG), for parsing the human body into parts and recovering their poses. Our method represents the human b...
Long Zhu, Yuanhao Chen, Yifei Lu, Chenxi Lin, Alan...
ICML
2006
IEEE
14 years 8 months ago
Learning a kernel function for classification with small training samples
When given a small sample, we show that classification with SVM can be considerably enhanced by using a kernel function learned from the training data prior to discrimination. Thi...
Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall
DAGM
2008
Springer
13 years 9 months ago
A Multiple Kernel Learning Approach to Joint Multi-class Object Detection
Most current methods for multi-class object classification and localization work as independent 1-vs-rest classifiers. They decide whether and where an object is visible in an imag...
Christoph H. Lampert, Matthew B. Blaschko
CVPR
2009
IEEE
15 years 2 months ago
Contextual Classification with Functional Max-Margin Markov Networks
We address the problem of label assignment in computer vision: given a novel 3-D or 2-D scene, we wish to assign a unique label to every site (voxel, pixel, superpixel, etc.). To...
Daniel Munoz, James A. Bagnell, Martial Hebert, Ni...