FUZZY EDGE IMAGE MATCHING ALGORITHM FOR SQUID SPECIES IDENTIFICATION
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Published:
Oct 31, 2020
Keywords:
Squid species Shape extraction Fuzzy Edge Map similarity matching Performance evaluation
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Abstract
Abstract: The image features play important role in matching system. The effectiveness of these Squid species features depends on the global features. The identification of Squid species requires information of their morphology. Body shape is very useful to characterize the one species to another species. In Shape extraction, edge detection is an important aspect. Edge is an important visual feature and it represents visual information with a limited number of pixels. While considering the morphology of Squid, it can have uncertainty due to climatic conditions. Hence, in this study feature extraction is done by fuzzy edge map. In this paper we proposed Fuzzy Image Edge Image Matching Algorithm (FEIMA) for Squid species identification. Similarity metric is used for matching of query and the candidate images in the database and it finally displays the class of species. The proposed algorithm performance is calculated by using Average of precision and recall.
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How to Cite
bindu, K. H., Jyothi, S., & Mamatha, D. (2020). FUZZY EDGE IMAGE MATCHING ALGORITHM FOR SQUID SPECIES IDENTIFICATION. Malaysian Journal of Science, 39(3), 95–103. https://doi.org/10.22452/mjs.vol39no3.8
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Original Articles
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References
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Hiremath P.S, Jagadeesh Pujari (2008). Content based Image Retrieval using color boosted Salient Points and Shape features of an image. International Journal of Image Processing, 2(1): 10-17.
Jun Zhang and Lei Ye (2009). Ranking Method for Optimizing Precision/Recall of Content-Based Image Retrieval. IEEE Digital Xplore, DOI: 10.1109/UIC-ATC.2009.9.
Kenneth H. L. Ho, Noboru Ohnishi (2006). FEDGE - Fuzzy Edge Detection by Fuzzy Categorization and Classification of Edges, DOI: 10.1007/3-540-62474-0_14.
Pooja R Bhat (2017). Content Based Image Retrieval using Shape Features Extracted with Morphological Transformation and Block Truncation Coding. International Journal of Advanced Research in Basic Engineering Sciences and Technology (IJARBEST), ISSN(Online) : 2456-5717, Vol. 3, Special Issue 23.
Shashank Mathur and Anil Ahlawat (2008). Application of fuzzy logic on image edge detection. International Book Series :Information Science and Computing.
Shaveta Malik, Tapas Kumar (2016). Various Edge Detection Techniques on different Categories of Fish. International Journal of Computer Applications, ISSN.0975 – 8887, Vol. 135, No.7.
Tizhoosh,H.R. Fast Fuzzy Edge Detection. IEEE 0-7803-7461-4/02.
Wang Xiaoling and XieKanglin(2005). Application of the fuzzy logic in content-based image retrieval. Journal of Computer Science & Technology, 5.
Xiaogbin Wang, Baokui Li, QingboGeng (2013). Runway Detection and Tracking for Unmanned Aerial Vehicle Based on an Improved Canny Edge Detection Algorithm. Signal & Image Processing : An International Journal (SIPIJ), IEEE Digital Xplore, DOI 10.1109/IHMSC.2012.132, Vol.4, No.3.
Anusha J.R., Albin T. Fleming (2014). Cephalopod: Squid Biology, Ecology and Fisheries in Indian waters . International Journal of Fisheries and Aquatic Studies, 1(4): 41-50.
Cephalopod Bionomics. Fisheries And Resources of The Exclusive Economic Zone Of India Edited By : E. G. Silas, June 1985.
Diksha Kurchaniya1, Punit Kumar Johari (2017). Analysis of Different
Similarity Measures in Image Retrieval Based on Texture and Shape. International Research Journal of Engineering and Technology (IRJET). e-ISSN: 2395 -0056, Vol. 04, Issue: 04.
Felicitas Perez-Ornelas, Olivia Mendoza1, Patricia Melin, Juan R. Castro, Antonio Rodriguez-Diaz, Oscar Castillo (2015). Fuzzy Index to Evaluate Edge Detection in Digital Images. PLOS ONE | DOI:10.1371/journal.pone.0131161, June.
Hiremath P.S, Jagadeesh Pujari (2008). Content based Image Retrieval using color boosted Salient Points and Shape features of an image. International Journal of Image Processing, 2(1): 10-17.
Jun Zhang and Lei Ye (2009). Ranking Method for Optimizing Precision/Recall of Content-Based Image Retrieval. IEEE Digital Xplore, DOI: 10.1109/UIC-ATC.2009.9.
Kenneth H. L. Ho, Noboru Ohnishi (2006). FEDGE - Fuzzy Edge Detection by Fuzzy Categorization and Classification of Edges, DOI: 10.1007/3-540-62474-0_14.
Pooja R Bhat (2017). Content Based Image Retrieval using Shape Features Extracted with Morphological Transformation and Block Truncation Coding. International Journal of Advanced Research in Basic Engineering Sciences and Technology (IJARBEST), ISSN(Online) : 2456-5717, Vol. 3, Special Issue 23.
Shashank Mathur and Anil Ahlawat (2008). Application of fuzzy logic on image edge detection. International Book Series :Information Science and Computing.
Shaveta Malik, Tapas Kumar (2016). Various Edge Detection Techniques on different Categories of Fish. International Journal of Computer Applications, ISSN.0975 – 8887, Vol. 135, No.7.
Tizhoosh,H.R. Fast Fuzzy Edge Detection. IEEE 0-7803-7461-4/02.
Wang Xiaoling and XieKanglin(2005). Application of the fuzzy logic in content-based image retrieval. Journal of Computer Science & Technology, 5.
Xiaogbin Wang, Baokui Li, QingboGeng (2013). Runway Detection and Tracking for Unmanned Aerial Vehicle Based on an Improved Canny Edge Detection Algorithm. Signal & Image Processing : An International Journal (SIPIJ), IEEE Digital Xplore, DOI 10.1109/IHMSC.2012.132, Vol.4, No.3.