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» Spatial Representation for Efficient Sequence Classification
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BIOINFORMATICS
2005
71views more  BIOINFORMATICS 2005»
13 years 7 months ago
Semi-supervised protein classification using cluster kernels
A key issue in supervised protein classification is the representation of input sequences of amino acids. Recent work using string kernels for protein data has achieved state-of-t...
Jason Weston, Christina S. Leslie, Eugene Ie, Deng...
CVPR
2010
IEEE
14 years 1 months ago
One-Shot Multi-Set Non-rigid Feature-Spatial Matching
We introduce a novel framework for nonrigid feature matching among multiple sets in a way that takes into consideration both the feature descriptor and the features spatial arra...
Marwan Torki and Ahmed Elgammal
ICIP
2007
IEEE
14 years 9 months ago
Blind Audiovisual Source Separation using Sparse Representations
In this work we present a method to jointly separate active audio and visual structures on a given mixture. Blind Audiovisual Source Separation is achieved exploiting the coherenc...
Anna Llagostera Casanovas, Gianluca Monaci, Pierre...
BMCBI
2004
208views more  BMCBI 2004»
13 years 7 months ago
Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures
Background: Hidden Markov Models (HMMs) have proven very useful in computational biology for such applications as sequence pattern matching, gene-finding, and structure prediction...
Vadim Alexandrov, Mark Gerstein
MMS
2006
13 years 7 months ago
View-invariant motion trajectory-based activity classification and recognition
Motion trajectories provide rich spatio-temporal information about an object's activity. The trajectory information can be obtained using a tracking algorithm on data streams ...
Faisal I. Bashir, Ashfaq A. Khokhar, Dan Schonfeld