Abstract
Plankton are fundamental and essential to marine ecosystem, and its survey is significant for sustainable development and ecosystem balance of oceans. The large amount of plankton species and complex relationship among different classes bring difficulty for us to design an automatic plankton classification system. Thus, we develop our model based on convolutional neural network and aim to overcome these shortages. We consider two different ways to extract global and local features to describe shape and texture information of plankton. Furthermore, we design a pyramid fully connected structure to merge different inner products from each sub networks. The experimental results prove our model can take advantage of multiple features and performs better than original convolutional neural network.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61271406, 61301240, and the Fundamental Research Funds for the Central Universities under Grant No. 201562023.
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Dai, J., Yu, Z., Zheng, H., Zheng, B., Wang, N. (2017). A Hybrid Convolutional Neural Network for Plankton Classification. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10118. Springer, Cham. https://doi.org/10.1007/978-3-319-54526-4_8
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