Working Paper Article Version 1 This version is not peer-reviewed

Pre-Computation of Image Features for the Classification of Dynamic Properties in Waves

Version 1 : Received: 4 October 2021 / Approved: 11 October 2021 / Online: 11 October 2021 (15:49:36 CEST)

How to cite: Smith, R.; Dias, F.; Facciolo, G.; Murphy, B. Pre-Computation of Image Features for the Classification of Dynamic Properties in Waves. Preprints 2021, 2021100170 Smith, R.; Dias, F.; Facciolo, G.; Murphy, B. Pre-Computation of Image Features for the Classification of Dynamic Properties in Waves. Preprints 2021, 2021100170

Abstract

The use of convolutional neural networks (CNNs) in image classification has become the standard method of approaching computer vision problems. Here we apply pre-trained networks to classify images of non-breaking, plunging and spilling breaking waves. The CNNs are used as basic feature extractors and a classifier is then trained on top of these networks. The dynamic nature of breaking waves is exploited by using image sequences to gain extra information and improve the classification results. We also see improved classification performance in using pre-computed image features such as the optical flow between image pairs. The inclusion of the dynamic information improves the classification between breaking wave classes. We also provide corrections to the methodology from the article from which the data originates to achieve a more accurate assessment of performance.

Keywords

breaking waves; optical flow; convolutional neural networks; image classification

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