Photo Identification of Sea Turtles Using AlexNet and Multi-Class SVM

被引：1

作者：

Yussof, Wan Nural Jawahir H. J. Wan ^{[1
]}

Shaharudin, Nurfarahim ^{[1
]}

Hitam, Muhammad Suzuri ^{[1
]}

Awalludin, Ezmahamrul Afreen ^{[2
]}

Rusli, Mohd Uzair ^{[3
]}

Hoh, Daphne Z. ^{[4
]}

机构：

[1] Univ Malaysia Terengganu, Fac Ocean Engn Technol & Informat, Terengganu 21030, Malaysia

[2] Univ Malaysia Terengganu, Fac Fisheries & Food Sci, Terengganu 21030, Malaysia

[3] Univ Malaysia Terengganu, Inst Oceanog, Terengganu 21030, Malaysia

[4] Acad Sinica, Biodivers Res Ctr, Taipei, Taiwan

来源：

KNOWLEDGE INNOVATION THROUGH INTELLIGENT SOFTWARE METHODOLOGIES, TOOLS AND TECHNIQUES (SOMET_20) | 2020年 / 327卷

关键词：

Photo identification; Deep Learning; Convolutional Neural Network; Support Vector Machine; Transfer Learning; PHOTOGRAPHIC IDENTIFICATION; SCALE PATTERNS; PHOTOIDENTIFICATION; VALIDATION;

D O I：

10.3233/FAIA200549

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Up to now, identification of sea turtle species mainly for tracking the population usually relied on flipper tags or through other physical markers. However, this approach is not practical due to the missing tags over some period. Due to this matter, we propose a photo identification system of the individual sea turtle based on the convolutional neural network (CNN) using a pre-trained AlexNet CNN and error-correcting output codes (ECOC) SVM. Experiments were performed on 300 images obtained from Biodiversity Research Center, Academia Sinica, Taiwan. Using Alexnet and ECOC SVM, the overall accuracy achieved is 62.9%. The results indicate that features obtained from the CNN are capable of identifying photo of sea turtles.

引用

页码：23 / 31

页数：9

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