Analysis of color feature extraction techniques for Fish Species Identification
Resumo
Color recognition is an important step for computer vision to be able to recognize objects in the most different environmental conditions. Classifying objects by color using computer vision is a good alternative for different color conditions such as the aquarium. In which it is possible to use resources of a smartphone with real-time image classification applications. This paper presents some experimental results regarding the use of five different feature extraction techniques to the problem of fish species identification. The feature extractors tested are the Bag of Visual Words (BoVW), the Bag of Colors (BoC), the Bag of Features and Colors (BoFC), the Bag of Colored Words (BoCW), and the histograms HSV and RGB color spaces. The experiments were performed using a dataset, which is also a contribution of this work, containing 1120 images from fishes of 28 different species. The feature extractors were tested under three different supervised learning setups based on Decision Trees, K-Nearest Neighbors, and Support Vector Machine. From the attribute extraction techniques described, the best performance was BoC using the Support Vector Machines as a classifier with an FMeasure of 0.90 and AUC of 0.983348 with a dictionary size of 2048.
Palavras-chave:
Aquarium Dataset, Fish Image Classification, Machine Learning, Point of Interest, Color Descriptor
Referências
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M. Friedman, “A comparison of alternative tests of significance for the problem of m rankings,” The Annals of Mathematical Statistics, no. 1, pp. 86–92. [Online]. Available: http://www.jstor.org/stable/2235971
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D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vision, vol. 60, no. 2, pp. 91–110, Nov. 2004. [Online]. Available: http://dx.doi.org/10.1023/B:VISI.0000029664.99615.94
L. F. J. W. G. Csurka, C. Dance and C. Bray, “Visual categorization with bags of keypoints,” Workshop on statistical learning in computer vision, vol. 1, pp. 1–22, 2004.
A. Bahri and H. Zouaki, “A surf-color moments for images retrieval based on bag-of-features,” in European Journal of Computer Science and Information Technology Vol.1 No.1, pp.11-22, June 2013. Department of Mathematics and computer science, Faculty of Science, El Jadida, Morocco: EAJ, Jun. 2013, pp. 11–22. [Online]. Available: http://www.eajournals.org/journals/european-journal-of-computerscience-and-information-technology-ejcsit/vol-1-issue-1-june-2013/asurf-color-moments-for-images-retrieval-based-on-bag-of-features/
C. S. Joost van de Weijer, “Coloring local feature extraction,” European Conference on Computer Vision (ECCV ’06), pp. 334–348, 2006.
J. van de Weijer1 and F. S. Khan, “Fusing color and shape for bag-ofwords based object recognition,” Computational Color Imaging. CCIW 2013. Lecture Notes in Computer Science, vol. 7786, 2013.
C. Wengert, M. Douze, and H. J´egou, “Bag-of-colors for improved image search,” in MM 2011 - 19th ACM International Conference on Multimedia. Scottsdale, United States: ACM, Nov. 2011, pp. 1437– 1440, qUAERO. [Online]. Available: http://hal.inria.fr/inria-00614523
S. Sural, Gang Qian, and S. Pramanik, “Segmentation and histogram generation using the hsv color space for image retrieval,” in Proceedings. International Conference on Image Processing, vol. 2, 2002, pp. II–II.
R. Chakravarti and X. Meng, “A study of color histogram based image retrieval,” in 2009 Sixth International Conference on Information Technology: New Generations, 2009, pp. 1323–1328.
J. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993.
V. N. Vapnik, “An overview of statistical learning theory,” Trans. Neur. Netw., vol. 10, no. 5, pp. 988–999, Sep. 1999. [Online]. Available: http://dx.doi.org/10.1109/72.788640
N. Altman, “An introduction to kernel and nearest neighbor nonparametric regression,” The American Statistician, vol. 46, pp. 175—-185, 1992.
A. Rova, G. Mori, and L. M. Dill, “One fish, two fish, butterfish, trumpeter: Recognizing fish in underwater video,” in In IAPR Conference on Machine Vision Applications, 2007.
M. S. Nery, A. M. Machado, M. F. M. Campos, F. L. C. Pádua, R. Carceroni, and J. P. Queiroz-Neto, “Determining the appropriate feature set for fish classification tasks,” Graphics, Patterns and Images, SIBGRAPI Conference on, vol. 0, pp. 173–180, 2005.
M. T. A. Rodrigues, “Classifição automática de espécies de peixes baseada em técnicas robustas para extração de caracterıisticas e sistemas imunológicos artificiais,” Mestrado, Belo Horizonte : Centro Federal de Educacao Tecnologica de Minas Gerais, 2009.
I. Jolliffe, Principal component analysis, ser. Springer series in statistics. Springer-Verlang, 1986. [Online]. Available: http://books.google.com.br/books?id=cN5UAAAAYAAJ
J. Matai, R. Kastner, G. R. Cutter, and D. A. Demer, “Automated techniques for detection and recognition of fishes using computer vision algorithms,” 2012.
F. Shafait, A. Mian, M. Shortis, B. Ghanem, P. F. Culverhouse, D. Edgington, D. Cline, M. Ravanbakhsh, J. Seager, and E. S. Harvey, “Fish identification from videos captured in uncontrolled underwater environments,” ICES Journal of Marine Science, vol. 73, no. 10, pp. 2737–2746, 07 2016. [Online]. Available: https: //doi.org/10.1093/icesjms/fsw106
N. Sengar, M. K. Dutta, and B. Sarkar, “Computer vision based technique for identification of fish quality after pesticide exposure,” International Journal of Food Properties, vol. 20, no. sup2, pp. 2192– 2206, 2017. [Online]. Available: https://doi.org/10.1080/10942912.2017.1368553
D. Rathi, S. Jain, and S. Indu, “Underwater fish species classification using convolutional neural network and deep learning,” 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR), Dec 2017. [Online]. Available: http://dx.doi.org/10.1109/ICAPR.2017.8593044
V. Allken, N. O. Handegard, S. Rosen, T. Schreyeck, T. Mahiout, and K. Malde, “Fish species identification using a convolutional neural network trained on synthetic data,” ICES Journal of Marine Science, vol. 76, no. 1, pp. 342–349, 10 2018. [Online]. Available: https://doi.org/10.1093/icesjms/fsy147
S. Cui, Y. Zhou, Y. Wang, and L. Zhai, “Fish detection using deep learning,” Applied Computational Intelligence and Soft Computing, vol. 2020, p. 3738108, Jan 2020. [Online]. Available: https: //doi.org/10.1155/2020/3738108
M. A. Iqbal, Z. Wang, Z. A. Ali, and S. Riaz, “Automatic fish species classification using deep convolutional neural networks,” Wireless Personal Communications, Aug 2019. [Online]. Available: https://doi.org/10.1007/s11277-019-06634-1
S. Z. S. Z. H. S. A. U. R. S. A. C. B. Hafiz Tayyab Raufa, M. Ikram Ullah Lalia, “Visual features based automated identification of fish species using deep convolutional neural networks,” Computers and Electronics in Agriculture, vol. 167, no. 105075, 2019.
M. I. I M Yusup and I. Jaya, “Real-time reef fishes identification using deep learning,” IOP Conference Series: Earth and Environmental Science, vol. 429, 2019.
C.-J. Chang, Chih-Chung e Lin, LIBSVM: A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology, 2011.
M. Friedman, “A comparison of alternative tests of significance for the problem of m rankings,” The Annals of Mathematical Statistics, no. 1, pp. 86–92. [Online]. Available: http://www.jstor.org/stable/2235971
M. Hollander and D. Wolfe, Nonparametric Statistical Methods, ser. A Wiley-Interscience publication. Wiley, 1999. [Online]. Available: http://books.google.com.br/books?id=RJAQAQAAIAAJ
Publicado
07/10/2020
Como Citar
FREITAS, Uéliton; PACHE, Marcio; GONÇALVES, Wesley; MATSUBARA, Edson; SABINO, José; SANT'ANA, Diego; PISTORI, Hemerson.
Analysis of color feature extraction techniques for Fish Species Identification. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 16. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 140-145.
DOI: https://doi.org/10.5753/wvc.2020.13495.
