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» Spatial Representation for Efficient Sequence Classification
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BIOINFORMATICS
2005
71views more  BIOINFORMATICS 2005»
14 years 9 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
15 years 3 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
15 years 11 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»
14 years 9 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
14 years 9 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