Shrimps classification based on multi-layer feature fusion

Xiaoxue Zhang; Zhiqiang Wei; Lei Huang; Xiaopeng Ji

doi:10.1117/12.2524161

6 May 2019 Shrimps classification based on multi-layer feature fusion

Xiaoxue Zhang, Zhiqiang Wei, Lei Huang, Xiaopeng Ji

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110690Q (2019) https://doi.org/10.1117/12.2524161
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

Abstract

This paper aims to highlight vision related tasks centered on “shrimps”. With the further study of computer vision of marine life, we show that “shrimps” has been largely neglected in comparison to other objects. In image classification, the degree of visual separation between different shrimp categories is highly uneven, the appearance of some categories in same genus is very similar, and it is more difficult to distinguish than others. Based on the classification model of traditional convolutional neural network, this paper presents a method of merging shallow and deep features extracting feature maps from different levels according to the characteristics of shrimp. In order to facilitate future shrimps-related research, we present our on-going effort in collecting a dataset in this paper, “ShrimpX”, that covers not only shrimps and lobsters living in the sea, but also some freshwater shrimps. The “ShrimpX” dataset contains a variety of shrimp images crawled from image search engines. Experimental results on the “ShrimpX” dataset demonstrate that the proposed method can effectively improve the accuracy.

Citation Download Citation

Xiaoxue Zhang, Zhiqiang Wei, Lei Huang, and Xiaopeng Ji "Shrimps classification based on multi-layer feature fusion", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110690Q (6 May 2019); https://doi.org/10.1117/12.2524161

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KEYWORDS

Feature extraction

Image classification

Convolutional neural networks

Image fusion

Convolution

Network architectures

Oceanography

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