Underwater Object Recognition Using KNN Classifier

Authors

  • Murugan S
  • Ganesh Babu T R
  • Srinivasan C

DOI:

https://doi.org/10.20894/IJMSR.117.009.003.007

Keywords:

Underwater Object recognition, DWT, KNN Classifier.

Abstract

In this paper, wavelet transform based method for underwater object recognition is presented. In this approach, input images are decomposed into sub-bands using multi resolutional analysis known as Discrete Wavelet Transform (DWT). Each sub-band in the decomposed image having valuable information about the image, the mean values of every sub-band are assumed as features. This system is tested on underwater object database. The database contains 200 pictures of 100 different objects. The database is considered for the classification based on K-Means Neural Network (KNN) classifier. The outcome shows that maximum recognition accuracy of 94.65% is obtained by this method.

Downloads

Download data is not yet available.

Author Biographies

Murugan S

Research Scholar Department of ECE, Maharishi University of Information Technology, Lucknow, Uttar Pradesh, India.

Ganesh Babu T R

Professor, Department of Electronics and Communication Engg., Muthayammal Engineering College, India.

Srinivasan C

Research scholar Department of CSE, Maharishi University of Information Technology, Lucknow, Uttar Pradesh, India.

References

1. Kang, Sangseung, Kim, Jaehong, Lee, Jaeyeon, Kim, Kyekyung, (2012), “Object recognition for cell manufacturing system”, Biquitous Robots and Ambient Intelligence, pp 512-514.

2. C. A. Murthy and Naik, Sarif Kumar, (2007), "Distinct multicolored region descriptors for object recognition." Pattern Analysis and Machine Intelligence, IEEE Transactions on 29, no. 7, pp. 1291-1296.

3. Antonio Torralba, and Alan, S, Willsky, Choi, Myung Jin, (2012), "A tree-based context model for object recognition." Pattern Analysis and Machine Intelligence, IEEE Transactions on 34, no. 2, pp. 240-252.

4. Ling-Yu Duan, Tiejun Huang, and Wen Gao, Yang, Jingjing, (2012), "Group-sensitive multiple kernel learning for object recognition." Image Processing, IEEE Transactions on 21, no. 5, pp. 2838-2852.

5. Xiao, Zhi-Tao, Wu, Jun, (2010), “Video surveillance object recognition based on shape and color features”, Image and Signal Processing pp.451 – 454.

6. Dencker, Tobias, Roschani, Masoud, Beyerer, Jürgen, Huber, Marco F, (2012), “Bayesian active object recognition via Gaussian process regression”, Information Fusion, pp.1718 – 1725.

7. Mei, Wu, Yanling, Li, Guangda, Zhou, Xiang-Dong, Wang, (2010), “Object Recognition via Adaptive Multi-level Feature Integration”, 12th International Asia-Pacific on Web Conference, pp. 253-259.

8. Antonio, B, Willsky, Alan S, Choi, Myung Jin, Torralba, (2012), “A Tree-Based Context Model for Object Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34 , no. 2, pp.240-252.

Downloads

Published

2017-10-23

Issue

Vol. 9 No. 3 (2017)

Section

Articles

License

Authors need to sign following agreement with International Journal of MC Square Scientific Research before publishing their articles:

  • Authors need to return copyright form to Journal Editor-in-chief to proceed their articles for publication. Meantime, the journal licensed under a Creative Commons Attribution License, which permits other user to distribute the work with an acknowledgement of the authors for International Journal of MC Square Scientific Research.
  •  Authors are also able to share their separate, additional contractual arrangements for the non-restricted contribution of the journal with an acknowledgement of publication in International Journal of MC Square Scientific Research.
  • Authors are allowed and encouraged to share their work during the submission process for increasing citation and exploring to increase the paper availability in worldwide way. The Effect of Open Access.