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.

[1]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[2]  Yali Amit,et al.  Sequential Learning of Reusable Parts for Object Detection , 2003 .

[3]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[4]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[5]  Cordelia Schmid,et al.  Affine-invariant local descriptors and neighborhood statistics for texture recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[6]  Shahram Ebadollahi,et al.  Automatic view recognition in echocardiogram videos using parts-based representation , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[7]  James S. Duncan,et al.  Arrangement: A Spatial Relation Between Parts for Evaluating Similarity of Tomographic Section , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[9]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Stan Z. Li,et al.  FloatBoost learning and statistical face detection , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Pietro Perona,et al.  A Bayesian approach to unsupervised one-shot learning of object categories , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[12]  Antonio Torralba,et al.  Sharing features: efficient boosting procedures for multiclass object detection , 2004, CVPR 2004.

[13]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[14]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[15]  Y. LeCun,et al.  Learning methods for generic object recognition with invariance to pose and lighting , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[16]  Y. Freund,et al.  Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By , 2000 .

[17]  Zhuowen Tu,et al.  Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.