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PSIVT
2007
Springer
170views Multimedia» more  PSIVT 2007»
13 years 11 months ago
Markov Random Fields and Spatial Information to Improve Automatic Image Annotation
Content-based image retrieval (CBIR) is currently limited because of the lack of representational power of the low-level image features, which fail to properly represent the actual...
Carlos Hernández-Gracidas, Luis Enrique Suc...
CVPR
2009
IEEE
14 years 12 months ago
A Revisit of Generative Model for Automatic Image Annotation using Markov Random Fields
Much research effort on Automatic Image Annotation (AIA) has been focused on Generative Model, due to its well formed theory and competitive performance as compared with many we...
Yu Xiang (Fudan University), Xiangdong Zhou (Fudan...
3DOR
2008
13 years 7 months ago
Markov Random Fields for Improving 3D Mesh Analysis and Segmentation
Mesh analysis and clustering have became important issues in order to improve the efficiency of common processing operations like compression, watermarking or simplification. In t...
Guillaume Lavoué, Christian Wolf
CVPR
2010
IEEE
13 years 10 months ago
Semantic Context Modeling with Maximal Margin Conditional Random Fields for Automatic Image Annotation
Context modeling for Vision Recognition and Automatic Image Annotation (AIA) has attracted increasing attentions in recent years. For various contextual information and resources,...
Yu Xiang, Xiangdong Zhou, Zuotao Liu, Tat-seng chu...
ICIP
2007
IEEE
14 years 6 months ago
Automatic Parametrisation for an Image Completion Method Based on Markov Random Fields
Recently, a new exemplar-based method for image completion, texture synthesis and image inpainting was proposed which uses a discrete global optimization strategy based on Markov ...
Huy Tho Ho, Roland Göcke