基于卷积神经网络的少数民族头饰识别

doi:10.16180/j.cnki.issn1007-7820.2019.02.011

Abstract

Abstract:

The feature point of the traditional headdress image recognition method was extracted by the researchers manually. The system has some disadvantages including tedious preprocessing steps, high sample requirement and low accuracy. To solve these problems, the convolutional neural network was constructed to learn the deep features from image data. The CNN model selected the ReLU function with better sparsity to adjust the output, and used back propagation algorithm to optimize the network parameters.The softmax classifier was identified after the CNN model. The experimental results showed that the recognition rate of the system to the test set of headdress reached 96.25%. This method was proved to have good recognition accuracy and recognition efficiency.

Key words: CNN, minority headdress, feature extraction, image recognition, deep learning, Caffe

CLC Number:

TP391

LI Rongrui,SHI Lin,ZHAO Wei. Minority Headdress Recognition Based on Convolutional Neural Network[J].Electronic Science and Technology, 2019, 32(2): 51-55.

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References 16

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民族	测试数量	准确率
俄罗斯族	15	80%
苗族	28	92.9%
傈僳族	23	86.9%
水族	27	96.3%
其余8类	147	100%

Minority Headdress Recognition Based on Convolutional Neural Network

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