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» Unsupervised Learning of Invariant Features Using Video
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BMCBI
2007
178views more  BMCBI 2007»
13 years 7 months ago
SVM clustering
Background: Support Vector Machines (SVMs) provide a powerful method for classification (supervised learning). Use of SVMs for clustering (unsupervised learning) is now being cons...
Stephen Winters-Hilt, Sam Merat
MM
2006
ACM
178views Multimedia» more  MM 2006»
14 years 1 months ago
Multimedia signal processing for behavioral quantification in neuroscience
While there have been great advances in quantification of the genotype of organisms, including full genomes for many species, the quantification of phenotype is at a comparatively...
Peter Andrews, Haibin Wang, Dan Valente, Jih&egrav...
WACV
2008
IEEE
14 years 1 months ago
Likelihood Map Fusion for Visual Object Tracking
Visual object tracking can be considered as a figure-ground classification task. In this paper, different features are used to generate a set of likelihood maps for each pixel i...
Zhaozheng Yin, Fatih Porikli, Robert T. Collins
ECCV
2008
Springer
14 years 9 months ago
Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation
Abstract. Sparse signal models learned from data are widely used in audio, image, and video restoration. They have recently been generalized to discriminative image understanding t...
Julien Mairal, Marius Leordeanu, Francis Bach, Mar...
CIVR
2007
Springer
154views Image Analysis» more  CIVR 2007»
14 years 1 months ago
Representing shape with a spatial pyramid kernel
The objective of this paper is classifying images by the object categories they contain, for example motorbikes or dolphins. There are three areas of novelty. First, we introduce ...
Anna Bosch, Andrew Zisserman, Xavier Muñoz