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CIKM
2009
Springer
14 years 11 days ago
Probabilistic models for topic learning from images and captions in online biomedical literatures
Biomedical images and captions are one of the major sources of information in online biomedical publications. They often contain the most important results to be reported, and pro...
Xin Chen, Caimei Lu, Yuan An, Palakorn Achananupar...
CVPR
2010
IEEE
14 years 1 months ago
Many-to-one Contour Matching for Describing and Discriminating Object Shape
We present an object recognition system that locates an object, identifies its parts, and segments out its contours. A key distinction of our approach is that we use long, salien...
Praveen Srinivasan, Qihui Zhu, Jianbo Shi
IJCAI
2003
13 years 9 months ago
Hierarchical Hidden Markov Models for Information Extraction
Information extraction can be defined as the task of automatically extracting instances of specified classes or relations from text. We consider the case of using machine learni...
Marios Skounakis, Mark Craven, Soumya Ray
CVPR
2011
IEEE
12 years 11 months ago
A Hierarchical Conditional Random Field Model for Labeling and Segmenting Images of Street Scenes
Simultaneously segmenting and labeling images is a fundamental problem in Computer Vision. In this paper, we introduce a hierarchical CRF model to deal with the problem of labelin...
Qixing Huang, Mei Han, Bo Wu, Sergey Ioffe
BMCBI
2010
118views more  BMCBI 2010»
13 years 7 months ago
From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification
Background: Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limit...
Bram Slabbinck, Willem Waegeman, Peter Dawyndt, Pa...