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Shrimps classification based on multi-layer feature fusion
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Paper 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.

Paper Details

Date Published: 6 May 2019
PDF: 7 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110690Q (6 May 2019); doi: 10.1117/12.2524161
Show Author Affiliations
Xiaoxue Zhang, Ocean Univ. of China (China)
Zhiqiang Wei, Ocean Univ. of China (China)
Lei Huang, Ocean Univ. of China (China)
Xiaopeng Ji, Ocean Univ. of China (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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