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EMMCVPR
2005
Springer
14 years 2 months ago
Exploiting Inference for Approximate Parameter Learning in Discriminative Fields: An Empirical Study
Abstract. Estimation of parameters of random field models from labeled training data is crucial for their good performance in many image analysis applications. In this paper, we p...
Sanjiv Kumar, Jonas August, Martial Hebert
AVSS
2006
IEEE
14 years 15 days 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
BMCBI
2011
13 years 11 days ago
To aggregate or not to aggregate high-dimensional classifiers
Background: High-throughput functional genomics technologies generate large amount of data with hundreds or thousands of measurements per sample. The number of sample is usually m...
Cheng-Jian Xu, Huub C. J. Hoefsloot, Age K. Smilde
ICASSP
2011
IEEE
13 years 15 days ago
Beyond bag of words: Combining generative and discriminative models for natural scene categorization
This paper proposes a simple yet new and effective framework by combining generative model and discriminative model for natural scene categorization. A state-of-the-art approach f...
Zhen Li, Kim-Hui Yap, Xiao-Ming Chen
DAGM
2006
Springer
14 years 13 days ago
Cross-Articulation Learning for Robust Detection of Pedestrians
Recognizing categories of articulated objects in real-world scenarios is a challenging problem for today's vision algorithms. Due to the large appearance changes and intra-cla...
Edgar Seemann, Bernt Schiele