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» Invariances in kernel methods: From samples to objects
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MVA
2007
149views Computer Vision» more  MVA 2007»
13 years 10 months ago
Extracting Object Regions Using Locally Estimated Probability Density Functions
In this paper, a novel method for estimating a precise object region using a given rough object region is proposed. For determining whether each pixel belongs to an object or not,...
Hidenori Takeshima, Takashi Ida, Toshimitsu Kaneko
CIDM
2007
IEEE
14 years 29 days ago
Efficient Kernel-based Learning for Trees
Kernel methods are effective approaches to the modeling of structured objects in learning algorithms. Their major drawback is the typically high computational complexity of kernel ...
Fabio Aiolli, Giovanni Da San Martino, Alessandro ...
CVPR
2010
IEEE
14 years 5 months ago
Locally-Parametric Pictorial Structures
Pictorial structure (PS) models are extensively used for part-based recognition of scenes, people, animals and multi-part objects. To achieve tractability, the structure and param...
Benjamin Sapp, Chris Jordan, Ben Taskar
PAMI
2007
202views more  PAMI 2007»
13 years 8 months ago
Weighted Graph Cuts without Eigenvectors A Multilevel Approach
—A variety of clustering algorithms have recently been proposed to handle data that is not linearly separable; spectral clustering and kernel k-means are two of the main methods....
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis
ICCV
2011
IEEE
12 years 9 months ago
A Nonparametric Riemannian Framework on Tensor Field with Application to Foreground Segmentation
Background modelling on tensor field has recently been proposed for foreground detection tasks. Taking into account the Riemannian structure of the tensor manifold, recent resear...
Rui Caseiro, João F. Henriques, Pedro Martins, Jo...