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Fruit classification using computer vision and feedforward neural network
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DOI：10.1016/j.jfoodeng.2014.07.001.png)
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Fruit classification is a difficult challenge due to the numerous types of fruits. In order to recognize fruits more accurately, we proposed a hybrid classification method based on fitness-scaled chaotic artificial bee colony (FSCABC) algorithm and feedforward neural network (FNN). First, fruits images were acquired by a digital camera, and then the background of each image were removed by split-and-merge algorithm. We used a square window to capture the fruits, and download the square images to 256 x 256. Second, the color histogram, texture and shape features of each fruit image were extracted to compose a feature space. Third, principal component analysis was used to reduce the dimensions of the feature space. Finally, the reduced features were sent to the FNN, the weights/biases of which were trained by the FSCABC algorithm. We also used a stratified K-fold cross validation technique to enhance the generation ability of FNN. The experimental results of the 1653 color fruit images from the 18 categories demonstrated that the FSCABC-FNN achieved a classification accuracy of 89.1%. The classification accuracy was higher than Genetic Algorithm-FNN (GA-FNN) with 84.8%, Particle Swarm Optimization-FNN (PSO-FNN) with 87.9%, ABC-FNN with 85.4%, and kernel support vector machine with 88.2%. Therefore, the FSCABC-FNN was seen to be effective in classifying fruits. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
Color histogram
Unser's texture analysis
Shape feature
Feedforward neural network
Fitness-scaled chaotic artificial bee colony
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5.8
论文数:
1.1W
被引数:
3.3W
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