Coral Reef Image Classifications with Hybrid Methods
C. Padma Priya1, S. Muruganantham2
1C.Padma Priya, Research Scholar in Computer Science ST. Hindu College, Nagercoil, Affliated to Manonmanium Sundaranar University , Tirunelveli, Tamil Nadu India.
2Dr. S.Muruganantham, Associate Professor of Computer Science – ST. Hindu College, Nagercoil, Affliated to Manonmanium Sundaranar University, Tirunelveli, Tamil Nadu India.
Manuscript received on 15 September 2019 | Revised Manuscript received on 23 September 2019 | Manuscript Published on 11 October 2019 | PP: 1247-1254 | Volume-8 Issue-11S September 2019 | Retrieval Number: K125109811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1251.09811S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: There are several organisms on oceans. Among the organisms coral reefs are the one with 800 species. Classifying coral is a difficult task. Scientist classify the coral organism and put in to groups based on their characteristics. There are several machine learning algorithms are implemented to analyzer and classify the coral species. The main aim of this work is to effectively use handcrafted features with deep features for classifying the coral classes. Here the state of art feature descriptors such as Local Binary Pattern, Local Arc Pattern and Improved Webbers Binary Code are proposed to extract the features of coral. The results which obtained can be further improved by combining these local descriptors with convolution neural network .The feature extracted by above methods are classified using KNN and Random Forest. Experiments with these methods are conducted using EILAT dataset. The Experimental results obtained by these methods demonstrate the effectiveness and robustness of our proposed method.
Keywords: LBP, KNN, Random Forest, Local Arc Pattern, Improved Webers Binary code.
Scope of the Article: Classification