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ICCV
2005
IEEE
15 years 11 months ago
Building a Classification Cascade for Visual Identification from One Example
Object identification (OID) is specialized recognition where the category is known (e.g. cars) and the algorithm recognizes an object's exact identity (e.g. Bob's BMW). ...
Andras Ferencz, Erik G. Learned-Miller, Jitendra M...
73
Voted
PAMI
2002
98views more  PAMI 2002»
14 years 9 months ago
Extraction of Visual Features for Lipreading
The multimodal nature of speech is often ignored in human-computer interaction, but lip deformations and other body motion, such as those of the head, convey additional information...
Iain Matthews, Timothy F. Cootes, J. Andrew Bangha...
CORR
2008
Springer
170views Education» more  CORR 2008»
14 years 9 months ago
Fast Wavelet-Based Visual Classification
We investigate a biologically motivated approach to fast visual classification, directly inspired by the recent work [13]. Specifically, trading-off biological accuracy for comput...
Guoshen Yu, Jean-Jacques E. Slotine
ICMLA
2008
14 years 11 months ago
Predicting Algorithm Accuracy with a Small Set of Effective Meta-Features
We revisit 26 meta-features typically used in the context of meta-learning for model selection. Using visual analysis and computational complexity considerations, we find 4 meta-f...
Jun Won Lee, Christophe G. Giraud-Carrier
CVIU
2006
162views more  CVIU 2006»
14 years 9 months ago
Unsupervised scene analysis: A hidden Markov model approach
This paper presents a new approach to scene analysis, which aims at extracting structured information from a video sequence using directly low-level data. The method models the se...
Manuele Bicego, Marco Cristani, Vittorio Murino