Earticle

초록

영어

The analysis, recognition and perception of behavior has usually been a crucial task for researchers. The goal of this paper is to address the problem to recognize animal species, which has numerous applications in zoology, ecology, biology, and entertainment. Researchers used different machine learning approach for animal species recognition, however the researchers mostly used image data for this purpose and ignore the importance of audio data. In this work, our focus is to process multi modality (image and voice) dataset for animal species recognition. We proposed two different networks for animals’ audio and visual representation to recongize animals’ species. First network for animals’ audios classification that extract MFCC features, and these features is passed from four VGG style blocks while the second network extract visual features from images to classify according to their species. The experimental results demonstrated the effectiveness of the proposed model of achieved better performance in terms of classification accuracies.

목차

Abstract
I. INTRODUCTION
II. The proposed model
A. Visual Classification of Animal Species.
III. RESULTS AND EXPERIMENTS
A. Training Detail
B. Implementation Detail
C. Dataset Explanation
D. Experimental Results of the Proposed Model
IV. CONCLUSION
REFERENCES

키워드

저자

• Min Je Kim [ Sejong University ]
• Tanveer Hussain [ Sejong University ]
• Waseem Ullah [ Sejong University ]
• Hikmat Yar [ Sejong University ]
• Mi Young Lee [ Sejong University ]
• Muhammad Sajjad [ Islamia College Peshawar Peshawar, Pakistan ]
• Sung Wook Baik [ Sejong University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004