Sciweavers

Share
CVPR
2006
IEEE

Image-Based Multiclass Boosting and Echocardiographic View Classification

9 years 10 months ago
Image-Based Multiclass Boosting and Echocardiographic View Classification
We tackle the problem of automatically classifying cardiac view for an echocardiographic sequence as a multiclass object detection. As a solution, we present an imagebased multiclass boosting procedure. In contrast with conventional approaches for multiple object detection that train multiple binary classifiers, one per object, we learn only one multiclass classifier using the LogitBoosting algorithm. To utilize the fact that, in the midst of boosting, one class is fully separated from the remaining classes, we propose to learn a tree structure that focuses on the remaining classes to improve learning efficiency. Further, we accommodate the large number of background images using a cascade of boosted multiclass classifiers, which is able to simultaneously detect and classify multiple objects while rejecting the background class quickly. Our experiments on echocardiographic view classification demonstrate promising performances of image-based multiclass boosting.
Shaohua Kevin Zhou, J. H. Park, Bogdan Georgescu,
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2006
Where CVPR
Authors Shaohua Kevin Zhou, J. H. Park, Bogdan Georgescu, Dorin Comaniciu, Costas Simopoulos, Joanne Otsuki
Comments (0)
books