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Hierarchical Classification in AUV Imagery

PublicationsPosted by navid nourani Thu, November 07, 2013 04:41:25

M.S. Bewley, N. Nourani-Vatani, D. Rao, B. Douillard, O. Pizarro, S.B. Williams

Publication date

Dec 2013


Field and Service Robotics


In recent years, Autonomous Underwater Vehicles (AUVs) have been used extensively to gather imagery and other environmental variables for ocean monitoring. Processing of this vast amount of collected imagery to label content is difficult, expensive and time consuming. Because of this, typically only a small subset of images are labelled, and only at a small number of points. In order to make full use of the raw data returned from the AUV, this process needs to be automated. In this work the single species classification problem is extended to a multi-species classification problem following a taxonomical hierarchy. We demonstrate the application of techniques used in areas such as computer vision, text classification and medical diagnosis to the supervised hierarchical classification of benthic images. We also discuss critical aspects such as training topology and various prediction and scoring methodologies. An interesting aspect of the presented work is that the ground truth labels are sparse and incomplete, i.e. not all labels go to the leaf node, which brings with it other interesting challenges. We find that the best classification results are obtained using Local Binary Patterns (LBP), training a network of binary classifiers with probabilistic output, and applying “one-vs-rest” classification at each level of the hierarchy for prediction. This work presents a working solution that allows AUV images to be automatically labelled with the most appropriate node in a hierarchy of 19 species and morphologies. The result is that the output of the AUV system can include a semantic map using the taxonomy prescribed by marine scientists. This has the potential to not only reduce the manual labelling workload, but also to reduce the current dependence that marine scientists have on extrapolating information from a relatively small number of sparsely labelled points.

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