@inproceedings{60cc39f9f6eb4ecdb153ecb3790a5e68,
title = "Mackerel Classification Using Global and Local Features",
abstract = "Blue and chub mackerels are often caught at the same time, and their market prices are different. Thus, humans need to classify them by their hands. Therefore, the demand for automatic sorting machines of these mackerels using image processing technique is increasing. Classification for blue and chub mackerels is a challenging task due to their quite similar appearance. In this paper, we propose a method for classifying blue and chub mackerels by image processing technique. The difference of the two mackerels appears on the whole and the local specific parts. Therefore, we use global and local features that are extracted from the whole and the specific part in fish images, respectively. Experimental results showed that the proposed method is superior to the methods using only global or local feature.",
keywords = "blue mackerel, chub mackerel, fish classification, global feature, image processing, local feature",
author = "Yoshito Nagaoka and Tomo Miyazaki and Yoshihiro Sugaya and Shinichiro Omachi",
note = "Funding Information: ACKNOWLEDGMENT This work was partially supported by JSPS KAKENHI Grand Numbers 16H02841 and 16K00259. The data used for the experiments was provided with the cooperation of LATEST-SYSTEM Inc. Publisher Copyright: {\textcopyright} 2018 IEEE.; 23rd IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2018 ; Conference date: 04-09-2018 Through 07-09-2018",
year = "2018",
month = oct,
day = "22",
doi = "10.1109/ETFA.2018.8502584",
language = "English",
series = "IEEE International Conference on Emerging Technologies and Factory Automation, ETFA",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1209--1212",
booktitle = "Proceedings - 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation, ETFA 2018",
address = "United States",
}