A Feature Learning and Object Recognition Framework for Underwater Fish Images

DOI10.1109/TIP.2016.2535342zbMATH Open1408.94108arXiv1603.01696WikidataQ39956779 ScholiaQ39956779MaRDI QIDQ4616362

Kresimir Williams, Jenq-Neng Hwang, Meng-Che Chuang

Publication date: 4 February 2019

Published in: IEEE Transactions on Image Processing (Search for Journal in Brave)

Abstract: Live fish recognition is one of the most crucial elements of fisheries survey applications where vast amount of data are rapidly acquired. Different from general scenarios, challenges to underwater image recognition are posted by poor image quality, uncontrolled objects and environment, as well as difficulty in acquiring representative samples. Also, most existing feature extraction techniques are hindered from automation due to involving human supervision. Toward this end, we propose an underwater fish recognition framework that consists of a fully unsupervised feature learning technique and an error-resilient classifier. Object parts are initialized based on saliency and relaxation labeling to match object parts correctly. A non-rigid part model is then learned based on fitness, separation and discrimination criteria. For the classifier, an unsupervised clustering approach generates a binary class hierarchy, where each node is a classifier. To exploit information from ambiguous images, the notion of partial classification is introduced to assign coarse labels by optimizing the "benefit" of indecision made by the classifier. Experiments show that the proposed framework achieves high accuracy on both public and self-collected underwater fish images with high uncertainty and class imbalance.

Full work available at URL: https://arxiv.org/abs/1603.01696

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

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