Flower Image Classification Based on Convolutional Neural Network

doi:10.3969/j.issn.1006-2475.2018.09.011

Abstract

Abstract: Aiming at the problem of parameter redundancy and destruction of spatial structure information produced by fully connected layer in flower image classification using convolution neural network, this paper proposes an effective improvement method. Firstly, the n×n convolutional filters are replaced by 1×n and n×1 convolutional filters, then they are connected to the spatial pyramid pooling behind convolution layer to reduce the dimension and extract features, finally the probabilistic distribution is exported in Softmax classifier. Experimental results show that this method not only improves the accuracy, but also reduces the training time by half, which greatly improves the training speed.

Key words: convolutional neural network, flower image classification, fully connected layer, spatial pyramid pooling

CLC Number:

TP18

ZHANG Xiao-feng, LIU Hong-zheng. Flower Image Classification Based on Convolutional Neural Network[J]. Computer and Modernization, 2018, 0(09): 52-.

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