A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Semi-Supervised Adapted HMMs for Unusual Event Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
An online support vector machine for abnormal events detection
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Improved one-class SVM classifier for sounds classification
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
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In this paper, a new image based method for detecting and extracting events in noisy hydrophone data sequence is developed. The method relies on dominant orientation and its robust reconstruction based on mutual information (MI) measure. This new reconstructed dominant orientation map of the spectrogram image can provide key segments corresponding to various acoustic events and is robust to noise. The proposed method is useful for long-term monitoring and a proper interpretation for a wide variety of marine mammals and human related activities using hydrophone data. The experimental results demonstrate that this image based approach can efficiently detect and extract unusual events, such as whale calls from the highly noisy hydrophone recordings.