Feature Extraction and Recognition Based on the Biological Analysis

Abstract:

In this paper the extracted features including rectangularity,elongation, invariant moments and the four ratios of the stored product pests, which are the ratio of antennae area to torso area, the ratio of antennae perimeter to torso perimeter,the ratio of head and chest area to abdominal area, the ratio of head and chest perimeter to abdominal perimeter. Then these 13 characteristic parameters are input to BP neural network and SVM for recognition and classification. Form the results we can see that the 13 features in this paper can be well reflected the stable characteristic information of the stored product pests.

• Periodical: Applied Mechanics and Materials (Volumes 513-517)
• Pages: 3180-3183
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.513-517.3180
• Online since: February 2014
• Authors: Wen Cang Zhao*, Fan Wang
• Keywords: Biological Analysis, BP Neural Network (BPNN), Classification, Feature Extraction, Recognition, Stored Product Pests, Support Vector Machine (SVM)

References

[1] Shengfang Zhang, Yongping Liu , Zengqiang Wu. Chinese stored product beetles [M]. Beijing: China Agricultural Science and Technology Press, (1998).

[2] Keagy P M, Schatzki T F. Effect of image resolution on insect detection in wheat radiographs[J]. Cereal Chem, 1991, 68(4): 339-343.

[3] Keagy P M, Schatzki T F. Machine recognition of weevil damage in wheat radiographs[J]. INT SOC FOR OPTICAL ENGINEERING, BELLINGHAM, WA(USA), 1993, 1836: 108-119.

[4] Jordan, Jay B. Vision guided insect handling system, Modeling and Simulation[J]. Proceedings of the Annual Pittsburgh Conference, 1990, 21(5): 1995-20.

[5] Hanqing Zhao , Zuorui Shen, Xinwen Yu. Mathematical morphological features applied to automatic identification of insects [J]. China Agricultural University, 2002, 7 (3): 38-42.

[6] Hu M K. Visual pattern recognition by moment invariants[J]. Information Theory, IRE Transactions on, 1962, 8(2): 179-187.

DOI: 10.1109/tit.1962.1057692

[7] Liqun Han. Artificial neural network theory, design and application: artificial neural cells, artificial neural networks and artificial neural systems [M]. Beijing: Chemical Industry Press, (2002).

DOI: 10.5772/15984

[8] Bin ZOU, Xiaoyu LIAO Yongnian ZENG Lixia HUANG. An improved BP neural network based on evaluating and forecasting model of water quality in Second Songhua River of China[J]. Chinese Journal of Geochemistry, 2006, 25(B08): 167－167.

DOI: 10.1007/bf02840064

[9] Hagan M T, Demuth H B, Beale M H. Neural network design[M]. Boston London: Pws Pub, (1996).

[10] Haykin S, Shiwei Ye etc. Neural network theory [M]. Beijing: Mechanical Industry Press. (2004).

[11] Shawe-Taylor J, Cristianini N. Kernel methods for pattern analysis[M]. Cambridge university press, (2004).