SiamFishNet: The Deep Investigation of Siamese Fighting Fishes

Lawankorn Mookdarsanit, Pakpoom Mookdarsanit

Abstract


The Siamese fighting fish (or betta) can be seen as ASEAN signature and heritage that should be preserved and collected in form of an intelligent model. This paper is proposed to create the R-CNN based model to investigate what the breed of an unknown betta image is (using only an image), called “SiamFishNet”. The model is formulated from 87,560 betta images that cover 12 breed of bettas: (1.) Veil Tail, (2.) Crown Tail, (3.) Half-moon, (4.) Super-delta Tail, (5.) Rose Tail, (6.) Butterfly Tail, (7.) Dragon-scale, (8.) Bumble-bee, (9.) Paradise, (10.) Elephant-ear, (11.) Orchid, and (12.) Spade Tail, respectively. For verification, our model measured by average precision. The result shows that our model has the average precision as 84 %.

Full Text:

PDF

References


“The History of Betta Fighting Fish: Betta Origins,” [Online]. Available: http://www.bettafishcenter.com/Betta-Origins.shtml.

[Accessed: 7 August 2018].

B. Beolens, M. Watkins and M. Grayson, The Eponym Dictionary of Reptiles. Baltimore: Johns Hopkins University Press, 2011, pp.47.

“About Bettas and Betta History,” [Online]. Available: https://www.chrisbettaworld.com/about-bettas/.

[Accessed: 7 August 2018].

“James Bond: From Russia with Love,” [Online]. Available: http://www.007museum.com/from_russia_with_love.htm.

[Accessed: 7 August 2018].

J. Slabanja, B. Meden, P. Peer, A. Jaklič and F. Solina, "Segmentation and Reconstruction of 3D Models from a Point Cloud with Deep Neural Networks," 2018 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, 2018, pp. 118-123.

P. Mookdarsanit and S. Gertphol, "Light-weight operation of a failover system for Cloud computing," The 5th IEEE International Conference on Knowledge and Smart Technology, Thailand, 2013, pp. 42-46.

C. Leng, H. Zhang, B. Li, G. Cai, Z. Pei and L. He, "Local Feature Descriptor for Image Matching: A Survey," in IEEE Access, 2018.

P. Mookdarsanit, L. Soimart, M. Ketcham and N. Hnoohom, "Detecting Image Forgery Using XOR and Determinant of Pixels for Image Forensics," The 11th IEEE International Conference on Signal-Image Technology & Internet-Based Systems, Bangkok, 2015, pp. 613-616.

L. Soimart and P. Pongcharoen, “Multi-row Machine Layout Design using Aritificial Bee Colony,” 2011 International Conference on Economics and Business Information, Bangkok, 2011.

L. Soimart and P. Mookdarsanit, “An Admission Recommendation of High-school Students using Apriori Algorithm,” The 6th International Conference on Sciences and Social Sciences, 2016.

X. Li, M. Larson and A. Hanjalic, "Geo-Distinctive Visual Element Matching for Location Estimation of Images," in IEEE Transactions on Multimedia, vol. 20, no. 5, pp. 1179-1194, May 2018.

P. Mookdarsanit and M. Rattanasiriwongwut, “Location Estimation of a Photo: A geo-signature MapReduce Workflow,” in Engineering Journal, vol.21, no.3, pp.295-308, May 2017.

P. Mookdarsanit and M. Ketcham, “Image Location Estimation of Well-known Places from Multi-source based Information,” The 11th International Symposium on Natural Language Processing, Ayutthaya, 2016.

L. Soimart and P. Mookdarsanit, “Name with GPS Auto-tagging of Thai-tourist Attractions from An Image,” The 2017 Technology Innovation Management and Engineering Science International Conference, 2017.

P. Mookdarsanit and L. Mookdarsanit, “Contextual Image Classification towards Metadata Annotation of Thai-tourist Attractions,” in ITMSoc Transactions on Information Technology Management, vol.3, no.1, pp.32-40, 2018.

Z. Xu, J. Du, L. Ye and D. Fan, "Multi-feature indexing for image retrieval based on hypergraph," 2016 4th International Conference on Cloud Computing and Intelligence Systems (CCIS), Beijing, 2016, pp. 494-500.

P. Mookdarsanit and M. Rattanasiriwongwut, “GPS Determination of Thai-temple Arts from a Single Photo,” The 11th International Conference on on Applied Computer Technology and Information Systems, Bangkok, Thailand, 2017, pp. 42-47.

M. G. Abdelmonem, "Navigating virtual heritage applications for historic cities in the middle east," 2017 23rd International Conference on Virtual System & Multimedia (VSMM), Dublin, 2017, pp. 1-8.

P. Mookdarsanit and M. Rattanasiriwongwut, “MONTEAN Framework: A Magnificent Outstanding Native-Thai and Ecclesiastical Art Network,” in International Journal of Applied Computer Technology and Information Systems, vol.6, no.2, pp.17-22, 2017.

R. R. Atallah, A. Kamsin, M. A. Ismail, S. A. Abdelrahman and S. Zerdoumi, "Face Recognition and Age Estimation Implications of Changes in Facial Features: A Critical Review Study," in IEEE Access, vol. 6, pp. 28290-28304, 2018.

L. Soimart and P. Mookdarsanit, “Gender Estimation of a Portrait: Asian Facial-significance Framework,” The 6th International Conference on Sciences and Social Sciences, Mahasarakham, 2016.

P. Rakshit, S. Saha, A. Konar and A. K. Nagar, "Evolutionary Approach to Straight Line Approximation for Image Matching in Dance-Posture Recognition," 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil, 2018, pp. 1-8.

P. Mookdarsanit and L. Mookdarsanit, “An Automatic Image Tagging of Thai Dance’s Gestures,” Joint Conference on ACTIS & NCOBA, Ayutthaya, Thailand, January 2018, pp. 76-80.

P. Mookdarsanit and L. Mookdarsanit, “A Content-based Image Retrieval of Muay-Thai Folklores by Salient Region Matching,” in International Journal of Applied Computer Technology and Information Systems, vol.7, no.2, pp.21-26, 2018.

E. Aguilar, B. Remeseiro, M. Bolaños and P. Radeva, "Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants," in IEEE Transactions on Multimedia, vol. 20, no. 12, pp. 3266-3275, 2018.

L. Soimart and P. Mookdarsanit, “Ingredients estimation and recommendation of Thai-foods,” in SNRU Journal of Science and Technology, vol.9, no.2, pp.509-520, Jul. 2017.

P. Mookdarsanit and L. Mookdarsanit, “Name and Recipe Estimation of Thai-desserts beyond Image Tagging,” in Kasembundit Engineering Journal, vol.8, Special Issue, pp.193-203, May. 2018.

L. Soimart and M. Ketcham, “An efficient algorithm for earth surface interpretation from satellite imagery,” in Engineering Journal, vol.20, no.5, pp.215-228, Nov. 2016.

L. Soimart and M. Ketcham, “Hybrid of Pixel-based and Region-based Segmentation for Geology Exploration from Multi-spectral Remote Sensing,” The 11th International Symposium on Natural Language Processing, Ayutthaya, 2016.

M. Wang, Z. Dong, Y. Cheng and D. Li, "Optimal Segmentation of High-Resolution Remote Sensing Image by Combining Superpixels With the Minimum Spanning Tree," in IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 1, pp. 228-238, Jan. 2018.

L. Soimart and M. Ketcham, “The Segmentation of Satellite Image Using Transport Mean-shift Algorithm,” 13th International Conference on IT Applications and Management (ITAM-13), 2015, pp. 124-128.

L. Zheng, Y. Yang and Q. Tian, "SIFT Meets CNN: A Decade Survey of Instance Retrieval," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5, pp. 1224-1244, 1 May 2018.

A. A. Olaode, G. Naghdy and C. A. Todd, "Unsupervised Image Classification by Probabilistic Latent Semantic Analysis for the Annotation of Images," 2014 International Conference on Digital Image Computing: Techniques and Applications, Wollongong, NSW, Australia, 2014, pp. 1-8.

B. İşçįMEN, Y. Kutlu, A. N. Reyhaniye and C. Turan, "Image analysis methods on fish recognition," 2014 22nd Signal Processing and Communications Applications Conference (SIU), Trabzon, 2014, pp. 1411-1414.

Y. Nishida, T. Ura, T. Hamatsu, K. Nagahashi, S. Inaba and T. Nakatani, "Fish recognition method using vector quantization histogram for investigation of fishery resources," 2014 Oceans - St. John's, St. John's, NL, 2014, pp. 1-5.

M. Chuang, J. Hwang and K. Williams, "A Feature Learning and Object Recognition Framework for Underwater Fish Images," in IEEE Transactions on Image Processing, vol. 25, no. 4, pp. 1862-1872, April 2016.

K. Demertzis, L. Iliadis and V. Anezakis, "A deep spiking machine-hearing system for the case of invasive fish species," 2017 IEEE International Conference on Innovations in Intelligent Systems and Applications, Gdynia, 2017, pp. 23-28.

K. Wang, K. D. Do and L. Cui, "An underwater electrosensor for identifying objects of similar volume and aspect ratio using convolutional neural network," 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vancouver, BC, 2017, pp. 4963-4968.

R. Girshick, J. Donahue, T. Darrell and J. Malik, "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation," 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 580-587.

J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers and A. W. M. Smeulders, "Selective Search for Object Recognition," in International Journal of Computer Vision, vol. 104, no.2, pp. 154-171, 2013.

K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition," The 3rd International Conference on Learning Representations, San Diego, CA, 2015.

K. He, X. Zhang, S. Ren and J. Sun, "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification," 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp. 1026-1034.

X. Qi, T. Wang and J. Liu, "Comparison of Support Vector Machine and Softmax Classifiers in Computer Vision," 2017 Second International Conference on Mechanical, Control and Computer Engineering, Harbin, 2017, pp. 151-155.

G. Zhang, E. Tu and D. Cui, "Stable and improved generative adversarial nets (GANS): A constructive survey," 2017 IEEE International Conference on Image Processing, Beijing, 2017, pp. 1871-1875.

X. Wu, K. Xu and P. Hall, "A survey of image synthesis and editing with generative adversarial networks," in Tsinghua Science and Technology, vol. 22, no. 6, pp. 660-674, December 2017.


Refbacks

  • There are currently no refbacks.