Seabird image identification in natural scenes using Grabcut and combined features

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. (C) 2016 Elsevier B.V. All rights reserved.