research-article

Discriminative Features for Bird Species Classification

Authors:

Xiaoshuai SunAuthors Info & Claims

ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service

Pages 256 - 260

https://doi.org/10.1145/2632856.2632917

Published: 10 July 2014 Publication History

Abstract

Bird species classification has received more and more attention in the field of computer vision, for its promising applications in biology and environmental studies. Although methods derived from basic-level classification are introduced to bird species classification, most of them couldn't get a satisfied result due to the absence of discriminative features and quantization errors. In this paper, we introduce discriminative features for bird species classification based on parts of birds. We first crop and align the images, obtaining some patches specifying the parts of a bird. The patches are collected, forming some codebooks to learn the intermediate-level features using sparse coding algorithm. We then learn a model which characterize the discrimination of each part of every species of birds. Finally, the learned features combined with the model are concatenated to form the final representation for training and classification. We show the effectiveness of the discriminative features on the CUB-200-2011 dataset.

References

[1]

Liu, J., Kanazawa, A., Jacobs, D., & Belhumeur, P. (2012). Dog breed classification using part localization. In Computer Vision--ECCV 2012 (pp. 172--185). Springer Berlin Heidelberg.

Digital Library

[2]

Wah, C., Branson, S., Welinder, P., Perona, P., & Belongie, S. (2011). The caltech-ucsd birds-200-2011 dataset.

[3]

Nilsback, M. E., & Zisserman, A. (2008, December). Automated flower classification over a large number of classes. In Computer Vision, Graphics & Image Processing, 2008. ICVGIP'08. Sixth Indian Conference on (pp. 722--729). IEEE.

Digital Library

[4]

Müller, H., de Herrera, A. G. S., Kalpathy-Cramer, J., Demner-Fushman, D., Antani, S., & Eggel, I. (2012, September). Overview of the ImageCLEF 2012 Medical Image Retrieval and Classification Tasks. In CLEF (Online Working Notes/Labs/Workshop).

[5]

A. Angelova, S.H. Zhu. "Efficient object detection and segmentation for fine-grained recognition, "Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on. IEEE, 2013.

Digital Library

[6]

Yao, B., Khosla, A., & Fei-Fei, L. (2011, June). Combining randomization and discrimination for fine-grained image categorization. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on (pp. 1577--1584). IEEE.

Digital Library

[7]

Yang, S., Bo, L., Wang, J., & Shapiro, L. G. (2012). Unsupervised Template Learning for Fine-Grained Object Recognition. In NIPS (pp. 3131--3139).

[8]

Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D. M., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive psychology, 8(3), 382--439.

[9]

Gavves, E., Fernando, B., Snoek, C. G. M., Smeulders, A. W. M., & Tuytelaars, T. (2013). Fine-Grained Categorization by Alignments. status: published, 1--8.

[10]

Duan, K., Parikh, D., Crandall, D., & Grauman, K. (2012, June). Discovering localized attributes for fine-grained recognition. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on (pp. 3474--3481). IEEE.

Digital Library

[11]

Zhang, N., Farrell, R., & Darrell, T. (2012, June). Pose pooling kernels for sub-category recognition. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on (pp. 3665--3672). IEEE.

Digital Library

[12]

Berg, T., & Belhumeur, P. N. (2013, June). POOF: Part-based one-vs.-one features for fine-grained categorization, face verification, and attribute estimation. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on (pp. 955--962). IEEE.

Digital Library

[13]

Deng, J., Krause, J., & Fei-Fei, L. (2013, June). Fine-grained crowdsourcing for fine-grained recognition. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on (pp. 580--587). IEEE.

Digital Library

[14]

Wah, C., & Belongie, S. (2013, June). Attribute-Based Detection of Unfamiliar Classes with Humans in the Loop. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on (pp. 779--786). IEEE.

Digital Library

[15]

Wah, C., Branson, S., Perona, P., & Belongie, S. (2011, November). Multiclass recognition and part localization with humans in the loop. In Computer Vision (ICCV), 2011 IEEE International Conference on (pp. 2524--2531). IEEE.

Digital Library

[16]

Branson, S., Perona, P., & Belongie, S. (2011, November). Strong supervision from weak annotation: Interactive training of deformable part models. In Computer Vision (ICCV), 2011 IEEE International Conference on(pp. 1832--1839). IEEE.

Digital Library

[17]

Zhou, X., Yu, K., Zhang, T., & Huang, T. S. (2010). Image classification using super-vector coding of local image descriptors. In Computer Vision--ECCV 2010 (pp. 141--154). Springer Berlin Heidelberg.

Digital Library

Cited By

Shaik FV G(2024)Automated Bird Detection using using Snapshot Ensemble of Deep Learning Models2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)10.1109/IITCEE59897.2024.10467481(1-6)Online publication date: 24-Jan-2024
https://doi.org/10.1109/IITCEE59897.2024.10467481
Sivaranjani BKarpagavalli S(2023)A Survey and Analysis of Deep Learning Techniques for Bird Species Classification2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)10.1109/ICSCSS57650.2023.10169573(215-221)Online publication date: 14-Jun-2023
https://doi.org/10.1109/ICSCSS57650.2023.10169573
Kumar SKondaveerti H(2023)A Comparative Study on Deep Learning Techniques for Bird Species Recognition2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)10.1109/ICCT56969.2023.10075901(1-6)Online publication date: 19-Jan-2023
https://doi.org/10.1109/ICCT56969.2023.10075901
Show More Cited By

Index Terms

Discriminative Features for Bird Species Classification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
  2. Machine learning

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Published In

cover image ACM Other conferences

ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service

July 2014

430 pages

ISBN:9781450328104

DOI:10.1145/2632856

General Chairs:
Hanzi Wang
Xiamen University, China
,
Larry Davis
University of Maryland, USA
,
Program Chairs:
Wenwu Zhu
Tsinghua University, China
,
Stephan Kopf
University of Mannheim, Germany
,
Yanyun Qu
Xiamen University, China
,
Publications Chairs:
Jun Yu
Xiamen University, China
,
Jitao Sang
Institute of Automation, CAS, China
,
Tao Mei
Microsoft Research Asia, China

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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NSF of China: National Natural Science Foundation of China
Beijing ACM SIGMM Chapter

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 July 2014

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ICIMCS '14

ICIMCS '14: International Conference on Internet Multimedia Computing and Service

July 10 - 12, 2014

Xiamen, China

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Overall Acceptance Rate 163 of 456 submissions, 36%

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Citations

Cited By

Shaik FV G(2024)Automated Bird Detection using using Snapshot Ensemble of Deep Learning Models2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)10.1109/IITCEE59897.2024.10467481(1-6)Online publication date: 24-Jan-2024
https://doi.org/10.1109/IITCEE59897.2024.10467481
Sivaranjani BKarpagavalli S(2023)A Survey and Analysis of Deep Learning Techniques for Bird Species Classification2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS)10.1109/ICSCSS57650.2023.10169573(215-221)Online publication date: 14-Jun-2023
https://doi.org/10.1109/ICSCSS57650.2023.10169573
Kumar SKondaveerti H(2023)A Comparative Study on Deep Learning Techniques for Bird Species Recognition2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)10.1109/ICCT56969.2023.10075901(1-6)Online publication date: 19-Jan-2023
https://doi.org/10.1109/ICCT56969.2023.10075901
Kumar MYadav AKumar MYadav D(2023)Bird Species Classification from Images Using Deep LearningComputer Vision and Image Processing10.1007/978-3-031-31417-9_30(388-401)Online publication date: 7-May-2023
https://doi.org/10.1007/978-3-031-31417-9_30
MUTLU MÖZDEM KAKCAYOL M(2022)Derin Öğrenme ile Kuş Türü Sınıflandırma: Karşılaştırmalı Bir ÇalışmaBird Species Classification Using Deep Learning: A Comparative StudyPoliteknik Dergisi10.2339/politeknik.90493325:3(1251-1260)Online publication date: 1-Oct-2022
https://doi.org/10.2339/politeknik.904933
Ghosh SIslam M(2021)Convolutional Neural Network Based on HOG Feature for Bird Species Detection and ClassificationAdvances in Science, Technology and Engineering Systems Journal10.25046/aj0602856:2(733-745)Online publication date: Mar-2021
https://doi.org/10.25046/aj060285
Choudhary TGujar SPanchal KSarvjeet Mishra VGoswami A(2021)A Deep Learning-Based Transfer Learning Approach for the Bird Species ClassificationAdvanced Computing10.1007/978-981-16-0404-1_4(43-52)Online publication date: 11-Feb-2021
https://doi.org/10.1007/978-981-16-0404-1_4
Kumar ADas S(2019)Bird Species Classification Using Transfer Learning with Multistage TrainingComputer Vision Applications10.1007/978-981-15-1387-9_3(28-38)Online publication date: 15-Nov-2019
https://doi.org/10.1007/978-981-15-1387-9_3
Ghosh SIslam M(2019)Bird Species Detection and Classification Based on HOG Feature Using Convolutional Neural NetworkRecent Trends in Image Processing and Pattern Recognition10.1007/978-981-13-9181-1_32(363-373)Online publication date: 20-Jul-2019
https://doi.org/10.1007/978-981-13-9181-1_32
Pang CYao HSun XZhao SZhang Y(2018)Exploring part-aware segmentation for fine-grained visual categorizationMultimedia Tools and Applications10.5555/3288443.328848877:23(30291-30310)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.5555/3288443.3288488
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