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AVSS
2006
IEEE
15 years 5 months ago
Classification-Based Likelihood Functions for Bayesian Tracking
The success of any Bayesian particle filtering based tracker relies heavily on the ability of the likelihood function to discriminate between the state that fits the image well an...
Chunhua Shen, Hongdong Li, Michael J. Brooks
ICMLA
2008
15 years 3 months ago
Image Segmentation as Learning on Hypergraphs
In this paper, we propose to use hypergraphs as the model for images and pose image segmentation as a machine learning problem in which some pixels (called seeds) are labeled as t...
Lei Ding, Alper Yilmaz
AAAI
2006
15 years 2 months ago
Learning Partially Observable Action Schemas
We present an algorithm that derives actions' effects and preconditions in partially observable, relational domains. Our algorithm has two unique features: an expressive rela...
Dafna Shahaf, Eyal Amir
CVPR
2010
IEEE
15 years 9 months ago
Safety in Numbers: Learning Categories from Few Examples with Multi Model Knowledge Transfer
Learning object categories from small samples is a challenging problem, where machine learning tools can in general provide very few guarantees. Exploiting prior knowledge may be ...
Tatiana Tommasi, Francesco Orabona, Barbara Caputo
CORR
2010
Springer
253views Education» more  CORR 2010»
15 years 1 months ago
Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition
Adaptive sparse coding methods learn a possibly overcomplete set of basis functions, such that natural image patches can be reconstructed by linearly combining a small subset of t...
Koray Kavukcuoglu, Marc'Aurelio Ranzato, Yann LeCu...