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» Image Distance Using Hidden Markov Models
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ICIP
2003
IEEE
14 years 11 months ago
Feature selection for unsupervised discovery of statistical temporal structures in video
We present algorithms for automatic feature selection for unsupervised structure discovery from video sequences. Feature selection in this scenario is hard because of the absence ...
Lexing Xie, Shih-Fu Chang, Ajay Divakaran, Huifang...
BMCBI
2007
142views more  BMCBI 2007»
13 years 9 months ago
Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary informa
Background: In past number of methods have been developed for predicting subcellular location of eukaryotic, prokaryotic (Gram-negative and Gram-positive bacteria) and human prote...
Mamoon Rashid, Sudipto Saha, Gajendra P. S. Raghav...
PAKDD
2009
ACM
263views Data Mining» more  PAKDD 2009»
14 years 4 months ago
Spatial Weighting for Bag-of-Visual-Words and Its Application in Content-Based Image Retrieval
It is a challenging and important task to retrieve images from a large and highly varied image data set based on their visual contents. Problems like how to fill the semantic gap b...
Xin Chen, Xiaohua Hu, Xiajiong Shen
AND
2009
13 years 7 months ago
Edge noise in document images
A degradation model that describes many image degradations produced by desktop scanning is used to study the edge noise that is present in bilevel document images. The standard de...
Craig McGillivary, Chris Hale, Elisa H. Barney Smi...
CVPR
2007
IEEE
14 years 11 months ago
Learning Gaussian Conditional Random Fields for Low-Level Vision
Markov Random Field (MRF) models are a popular tool for vision and image processing. Gaussian MRF models are particularly convenient to work with because they can be implemented u...
Marshall F. Tappen, Ce Liu, Edward H. Adelson, Wil...