Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision
Image-Based Multiclass Boosting and Echocardiographic View Classification
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Characterizing spatio-temporal patterns for disease discrimination in cardiac echo videos
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Behavior and properties of spatio-temporal local features under visual transformations
Proceedings of the international conference on Multimedia
Automatic view recognition in echocardiogram videos using parts-based representation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
MCBR-CDS'12 Proceedings of the Third MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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Echocardiography plays an important part in diagnostic aid in cardiology. During an echocardiogram exam images or image sequences are usually taken from different locations with various directions in order to comprehend a comprehensive view of the anatomical structure of the 3D moving heart. The automatic classification of echocardiograms based on the viewpoint constitutes an essential step in a computer-aided diagnosis. The challenge remains the high noise to signal ratio of an echocardiography, leading to low resolution of echocardiograms. In this paper, a new synergy is proposed based on well-established algorithms to classify view positions of echocardiograms. Bags of Words (BoW) are coupled with linear SVMs. Sparse coding is employed to train an echocardiogram video dictionary based on a set of 3D SIFT descriptors of space-time interest points detected by a Cuboid detector. Multiple scales of max pooling features are applied to representat the echocardiogram video. The linear multiclass SVM is employed to classify echocardiogram videos into eight views. Based on the collection of 219 echocardiogram videos, the evaluation is carried out. The preliminary results exhibit 72% Average Accuracy Rate (AAR) for the classification with eight view angles and 90% with three primary view locations.