The Recognition of Maize seeds Based on Multi-scale Feature Fusion and Extreme Learning Machine

DOI

10.2991/icmeis-15.2015.72How to use a DOI?

Keywords

extreme learning machine; feature fusion; object recognition

Abstract

In recognizing traditional crops seeds like maize seeds, we usually use electrophoresis assay method, fluorescence scanning method and chemical assay method. These methods are destructive methods. They take a long time to detect and are demanding of professional background knowledge and hardware conditions etc. What’s more, these methods, based on BP neural network and support vector machine (SVM)while taking a long time to detect are less accurate in process of classification. In this paper, based on the computer vision technology, we proposed a new method for the classification of maize seeds, a method based on multi-scale feature fusion and extreme learning machine. First, we extract the multi-scale fusion feature of maize seeds. Second, based on extreme learning machine, we construct the classifier model of maize seed. Third, because of the window of image in the case of multi-scale detection has the problem of capturing the same object seed with many overlapping windows, we put forward a kind of window fusion algorithm to solve it. The simulation results show that: The method is able to identify the maize seeds accurately. Using this method the accuracy of classification of maize seeds can reached 97.66% and the error rate is less than 0.1%. Compared with the traditional methods, the method we proposed can improve the speed of detection and the accuracy of classification, and has no strict hardware requirements.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Mingzhi Du
AU  - Xiao Ke
AU  - Mingke Zhou
PY  - 2015/08
DA  - 2015/08
TI  - The Recognition of Maize seeds Based on Multi-scale Feature Fusion and Extreme Learning Machine
BT  - Proceedings of the 3rd International Conference on Mechanical Engineering and Intelligent Systems (ICMEIS 2015)
PB  - Atlantis Press
SP  - 391
EP  - 397
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmeis-15.2015.72
DO  - 10.2991/icmeis-15.2015.72
ID  - Du2015/08
ER  -