An automated event detection and classification system for abyssal time-series images of Station M, NE Pacific

Cline, Danelle E.; Edgington, D.R.; Smith, Ken; Vardaro, Michael; Kuhnz, Linda A.; Ellena, Jacob A.

doi:10.23919/oceans.2009.5422292

Cited by 2 publications

References 6 publications

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Linear Prediction Based Mixture Models for Event Detection in Video Sequences

Matern

Condurache

Mertins

2011

Pattern Recognition and Image Analysis

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Abstract. In this paper, we propose a method for the detection of irregularities in time series, based on linear prediction. We demonstrate how we can estimate the linear predictor by solving the Yule Walker equations, and how we can combine several predictors in a simple mixture model. In several tests, we compare our model to a Gaussian mixture and a hidden Markov model approach. We successfully apply our method to event detection in a video sequence.

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Linear Prediction Based Mixture Models for Event Detection in Video Sequences

Matern

Condurache

Mertins

2011

Pattern Recognition and Image Analysis

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On the impact of Citizen Science-derived data quality on deep learning based classification in marine images

et al. 2019

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The evaluation of large amounts of digital image data is of growing importance for biology, including for the exploration and monitoring of marine habitats. However, only a tiny percentage of the image data collected is evaluated by marine biologists who manually interpret and annotate the image contents, which can be slow and laborious. In order to overcome the bottleneck in image annotation, two strategies are increasingly proposed: “citizen science” and “machine learning”. In this study, we investigated how the combination of citizen science, to detect objects, and machine learning, to classify megafauna, could be used to automate annotation of underwater images. For this purpose, multiple large data sets of citizen science annotations with different degrees of common errors and inaccuracies observed in citizen science data were simulated by modifying “gold standard” annotations done by an experienced marine biologist. The parameters of the simulation were determined on the basis of two citizen science experiments. It allowed us to analyze the relationship between the outcome of a citizen science study and the quality of the classifications of a deep learning megafauna classifier. The results show great potential for combining citizen science with machine learning, provided that the participants are informed precisely about the annotation protocol. Inaccuracies in the position of the annotation had the most substantial influence on the classification accuracy, whereas the size of the marking and false positive detections had a smaller influence.

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An automated event detection and classification system for abyssal time-series images of Station M, NE Pacific

Cited by 2 publications

References 6 publications

Linear Prediction Based Mixture Models for Event Detection in Video Sequences

Linear Prediction Based Mixture Models for Event Detection in Video Sequences

On the impact of Citizen Science-derived data quality on deep learning based classification in marine images

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