Fish species classification in unconstrained underwater environments based on deep learning. (31st May 2016)

• Record Type: Journal Article
• Authors: Salman, Ahmad; Jalal, Ahsan; Shafait, Faisal; Mian, Ajmal; Shortis, Mark; Seager, James; Harvey, Euan
• Is Part Of: Limnology and oceanography, methods. Volume 14:Number 9(2016:Sep.)
• Page Start: 570
• Page End: 585
• Publication Date: 2016-05-31
• Subjects: Limnology -- Methodology -- Periodicals; Oceanography -- Methodology -- Periodicals; 551.48
• DOI: 10.1002/lom3.10113
• Languages: English
• ISSNs: 1541-5856

Abstract

Abstract: Underwater video and digital still cameras are rapidly being adopted by marine scientists and managers as a tool for non‐destructively quantifying and measuring the relative abundance, cover and size of marine fauna and flora. Imagery recorded of fish can be time consuming and costly to process and analyze manually. For this reason, there is great interest in automatic classification, counting, and measurement of fish. Unconstrained underwater scenes are highly variable due to changes in light intensity, changes in fish orientation due to movement, a variety of background habitats which sometimes also move, and most importantly similarity in shape and patterns among fish of different species. This poses a great challenge for image/video processing techniques to accurately differentiate between classes or species of fish to perform automatic classification. We present a machine learning approach, which is suitable for solving this challenge. We demonstrate the use of a convolution neural network model in a hierarchical feature combination setup to learn species‐dependent visual features of fish that are unique, yet abstract and robust against environmental and intra‐and inter‐species variability. This approach avoids the need for explicitly extracting features from raw images of the fish using several fragmented image processing techniques. As a result, we achieve a single and generic trained architecture with favorable performance even for sample images of fish species that have not been used in training. Using the LifeCLEF14 and LifeCLEF15 benchmark fish datasets, we have demonstrated results with a correct classification rate of more than 90%.