The use of Local Binary Pattern (LBP) feature extraction Members of the mud crab genus Scylla
Pages 202 - 206
Abstract
In the member of mud crab, species of Scylla are the most traded seafood commodity in Asia and the culturing practice is already done in most of Asia country a few years ago. The demand for mud crabs has increased rapidly over the last decade, providing great potential for the development of the mud crab aquaculture industry. But, there is still unsolved problem where limitation of knowledge in identification is limited due to similar colours and feature characteristic. Thus, this study proposed an automatic technique that can evaluate and produce the subset of mud crab genus Scylla features by using Local Binary Pattern (LBP) as a feature extraction tool. The main objective of the study is to find the optimal subset of mud crab genus Scylla features from a carapace images dataset. Based on 153 extracted features chosen by LBP features selection methods, the accuracy rates of three classification algorithms were obtained for analysis. The results from the MatLab experiment demonstrated that, the LBP method produced an accuracy under 60% for entire classifier.
References
[1]
Alok K.P, M. Manjurul Alam, M. Shahanul Islam, M. Afzal Hussain, Simon K. Das: Feeding behaviour of mud crab S. serrata in north of Sundarbans, Bangladesh. Aquaculture, Aquarium, Conservation & Legislation 3(11), 701--708 (2018)
[2]
Segura-García, Iris, Thu Yain Tun, and Stephen J. Box: Genetic characterization of the artisanal mud crab fishery in Myanmar. PloS one 13(9), 3190--3193 (2018)
[3]
Motoh, H.: Field guide for the edible crustacea of the Philippines. Aquaculture Department, Southeast Asian Fisheries Development Center, (1980)
[4]
Mandal, Anup and Varkey, Mathews and Sobhanan, Sobha P and Mani, Anjali K and Raj, Thampi Sam and Yohannan, C: Molecular genetic approaches to resolve taxonomical ambiguity of mud crab species (Genus Scylla) in Indian waters.Proceedings of the International Seminar-Workshop on Mud Crab Aquaculture and Fisheries Management, (2015)
[5]
Keenan, C.P., Davie, P.J.F., and Mann, D.L: A revision of the genus Scylla de Haan, 1833 (Crustacea: Decapoda: Brachyura: Portunidae. The Raffles Bulletin of Zoology 46, 217--245 (1998)
[6]
Fazhan, Hanafiah and Waiho, Khor and Ikhwanuddin, Mhd: Non-indigenous giant mud crab, Scylla serrata (Forskål, 1775)(Crustacea: Brachyura: Portunidae) in Malaysian coastal waters: a call for caution. Journal of Marine Biodiversity Records 10(1), 26 (2017)
[7]
Overton, J Lynne and Macintosh, Donald J: Estimated size at sexual maturity for female mud crabs (genus Scylla) from two sympatric species within Ban Don Bay, Thailand. Journal of Crustacean Biology 22(4), 790--797 (2002)
[8]
Naim, Darlina Md and Rosly, Hurul Adila-Aida Mohamad and Nor, Siti Azizah Mohd: Assessment of PhylogeneticInter-Relationships in Mud Crab Genus Scylla (Portunidae) Based on Mitochondrial DNA Sequence. International Conference on Applied Life Sciences, (2012)
[9]
Fuseya, Reiko and Watanabe, Seiichi: Genetic Varability in the Mud Crab Genus Scylla (Brachyura: Portunidae). Journal of Fisheries Science 62(5), 705--709 (1996)
[10]
Ikhwanuddin, M., Azmie, G., Juriah, H.M., Zakaria, M.Z., Ambak, M.A.: Biological information and population features of mud crab, genus Scylla from mangrove areas of Sarawak, Malaysia. Fisheries Research 108, 299--306 (2011)
[11]
Ojala, Timo and Pietik: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis & Machine Intelligence 7, 971--987 (2002)
[12]
Prathiba, T and Soniah Darathi, G: An efficient content based image retrieval using local tetra pattern. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 2(10), (2013)
[13]
Bashir, Ahmedelmubarak and Mustafa, Zeinab A and Abdelhameid, Islah and Ibrahem, Rimaz: Detection of malaria parasites using digital image processing. International Conference on Communication, Control, Computing and Electronics Engineering, (2017)
[14]
Shweta R. Astonkar, Dr. V. K. Shandilya: Detection and Analysis of Plant Diseases Using Image Processing Technique. International Research Journal of Engineering and Technology (IRJET) 5(4), 3190--3193 (2018)
[15]
Prathiba, T and Soniah Darathi, G: Texture Classification using Local Binary Patterns and Modular PCA. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 5(5), (2016)
Index Terms
- The use of Local Binary Pattern (LBP) feature extraction Members of the mud crab genus Scylla
Recommendations
Application of Feature Extraction and Classification Methods for Histopathological Image using GLCM, LBP, LBGLCM, GLRLM and SFTA
AbstractClassification of histopathologic images and identification of cancerous areas is quite challenging due to image background complexity and resolution. The difference between normal tissue and cancerous tissue is very small in some cases. So, the ...
Noise-tolerant texture feature extraction through directional thresholded local binary pattern
AbstractLocal binary pattern (LBP) is a multi-applicable texture descriptor applied in machine vision. Despite its outstanding abilities in revealing textural properties of image, it is sensitive to noise, due to its thresholding mechanism. To make LBP ...
Genetic based LBP feature extraction and selection for facial recognition
ACMSE '11: Proceedings of the 49th annual ACM Southeast ConferenceThis paper presents a novel approach to LBP feature extraction. Unlike other LBP feature extraction methods, we evolve the number, position, and the size of the areas of feature extraction. The approach described in this paper also attempts to minimize ...
Comments
Information & Contributors
Information
Published In
October 2019
253 pages
ISBN:9781450372534
DOI:10.1145/3369114
Copyright © 2019 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]
In-Cooperation
- Northumbria University: University of Northumbria at Newcastle
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 21 January 2020
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICAAI 2019
ICAAI 2019: 2019 The 3rd International Conference on Advances in Artificial Intelligence
October 26 - 28, 2019
Istanbul, Turkey
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 59Total Downloads
- Downloads (Last 12 months)3
- Downloads (Last 6 weeks)0
Reflects downloads up to 02 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in