Seabird image identification in natural scenes using Grabcut and combined features |
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| Affiliation: | 1. Cognitive Science Department, Xiamen University, Xiamen 361005, China;2. Fujian Key Laboratory of the Brain-like Intelligent Systems (Xiamen University), Xiamen 361005, China;1. Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA;2. College of Computer and Information Science, Northeastern University, Boston, MA, USA;1. Department of Pathophysiology, Wroclaw Medical University, Wroclaw, Poland;2. Center of Cardiac Prevention and Rehabilitation ‘Creator’ in Wroclaw, Wroclaw, Poland;3. Department of Normal Anatomy, Wroclaw Medical University, Wroclaw, Poland;4. Dietrich Bonhoeffer Klinikum Neubrandenburg, Germany;5. Department of Physiotherapy in Conservative and Operative Medicine, University School of Physical Education in Wrocław, Wroclaw, Poland;6. Ostrobramska Medical Center, Health Care Facility, Magodent, Warsaw, Poland;7. Department of Physiology, Wroclaw Medical University, Wroclaw, Poland;1. Centre for Ecology and Conservation, University of Exeter, Exeter, United Kingdom;2. FitzPatrick Institute of African Ornithology, DST-NRF Centre of Excellence, University of Cape Town, Cape Town, South Africa |
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| Abstract: | This paper proposes an automated seabird segmentation and identification method that applies to seabird images taken in natural scenes with a non-uniform and complex background. A variety of different bird postures appeared in natural scenes present different features from different points of view, even for the same posture. At first, the Grabcut method is introduced to segment seabird unit from a complicated background. Then, global features, namely the colour, shape and texture characteristics, and local features are integrated to describe the birds regarding various postures. Later, a combined recognition model, which is built using the k-Nearest Neighbor, Logistic Boost and Random Forest models by a voting mechanism that is designed for seabird identification. Finally, the efficiency and effectiveness of the proposed method in recognising seabirds were experimentally demonstrated. The experimental results on 900 field samples (6 seabird species, and 150 samples of each species) achieved a recognition accuracy of 88.1%, which indicates that the proposed method is effective for automated seabird identification. |
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