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» Using Maximum Entropy for Automatic Image Annotation
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IPMI
2009
Springer
14 years 2 months ago
Clustering of the Human Skeletal Muscle Fibers Using Linear Programming and Angular Hilbertian Metrics
In this paper, we present a manifold clustering method for the classification of fibers obtained from diffusion tensor images (DTI) of the human skeletal muscle. Using a linear ...
Radhouène Neji, Ahmed Besbes, Nikos Komodak...
CIVR
2010
Springer
254views Image Analysis» more  CIVR 2010»
14 years 21 days ago
Video scene analysis of interactions between humans and vehicles using event context
In this paper, we present a methodology to estimate a detailed state of a video scene involving multiple humans and vehicles. In order to annotate and retrieve videos containing a...
M. S. Ryoo, Jong Taek Lee, Jake K. Aggarwal
CBMS
2009
IEEE
14 years 2 months ago
Domain concept-based queries for cancer research data sources
Biomedical scientists generate, access, validate and interpret multiple distributed and heterogeneous data sets. Semantic annotations for these data sets are paramount for exchang...
Alejandra González Beltrán, Anthony ...
CLEF
2005
Springer
14 years 1 months ago
The CLEF 2005 Cross-Language Image Retrieval Track
Abstract. The purpose of this paper is to outline efforts from the 2005 CLEF crosslanguage image retrieval campaign (ImageCLEF). The aim of this CLEF track is to explore the use o...
Paul Clough, Henning Müller, Thomas Deselaers...
CVPR
2003
IEEE
14 years 10 months ago
An Efficient Approach to Learning Inhomogeneous Gibbs Model
Inhomogeneous Gibbs model (IGM) [4] is an effective maximum entropy model in characterizing complex highdimensional distributions. However, its training process is so slow that th...
Ziqiang Liu, Hong Chen, Heung-Yeung Shum