Earticle

초록

영어

This research is to present a new approach for the classification of the Penaeid Prawn Species. The extraction of Texture features based on the Gabor filter is proposed in this method. These extracted features are used for the classification of Penaeid Prawn Species based on Radial Basis Probabilistic Neural Networks and Support Vector Machines. The texture of the prawn image are extracted based on different scales and orientations by which mean and standard deviation are calculated. The resultant Gabor feature values are fed as input to Radial basis Probabilistic Neural Network Classifier for the classification of the species. The experimental results show the performance of the extracted feature vectors for Penaeid Prawn species recognition. The RBPNN gives better recognition when compared with Support vector machines.

목차

Abstract
 1. Introduction
 2. Image Acquisition
 3. Extraction of Features using Gabor Filter
 4. Methodology
  4.1. Radial Basis Probabilistic Network (RBPNN) Classifier
  4.2. Support Vector Machine
 5. Experimental and Results
 6. Conclusion
 References

키워드

저자

• V.Sucharita [ Department of Computer Science and Engineering, KL University, Vaddeswaram, AP, 522502, India ]
• P.Venkateswara Rao [ Department of Computer Science and Engineering, ASCET, Gudur, AP, India ]
• Debnath Bhattacharyya [ Department of Information Technology, Bharati Vidyapeeth University College of Engineering, Pune-411043, India ]
• Tai-hoon Kim [ Department of Convergence Security, Sungshin Women's University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605