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» Mercer Kernels for Object Recognition with Local Features
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ICPR
2008
IEEE
15 years 10 months ago
Semi-supervised learning by locally linear embedding in kernel space
Graph based semi-supervised learning methods (SSL) implicitly assume that the intrinsic geometry of the data points can be fully specified by an Euclidean distance based local ne...
Rujie Liu, Yuehong Wang, Takayuki Baba, Daiki Masu...
ICB
2007
Springer
176views Biometrics» more  ICB 2007»
15 years 8 months ago
A Novel Null Space-Based Kernel Discriminant Analysis for Face Recognition
The symmetrical decomposition is a powerful method to extract features for image recognition. It reveals the significant discriminative information from the mirror image of symmetr...
Tuo Zhao, Zhizheng Liang, David Zhang, Yahui Liu
ICAPR
2005
Springer
15 years 9 months ago
Missing Data Estimation Using Polynomial Kernels
Abstract. In this paper, we deal with the problem of partially observed objects. These objects are defined by a set of points and their shape variations are represented by a statis...
Maxime Berar, Michel Desvignes, Gérard Bail...
ICML
2005
IEEE
16 years 5 months ago
Weighted decomposition kernels
We introduce a family of kernels on discrete data structures within the general class of decomposition kernels. A weighted decomposition kernel (WDK) is computed by dividing objec...
Sauro Menchetti, Fabrizio Costa, Paolo Frasconi
ICPR
2004
IEEE
16 years 5 months ago
Object Recognition Using Segmentation for Feature Detection
: A new method is presented to learn object categories from unlabeled and unsegmented images for generic object recognition. We assume that each object can be characterized by a se...
Andreas Opelt, Axel Pinz, Michael Fussenegger, Pet...