Classification of flower image based on attention mechanism and multi-loss attention network

Abstract

The accurate classification of flower images is the prerequisite for flower plant management to artificial intelligence, how to use the machine to classify flowers automatically is the current hot issue to be solved. This paper first introduced the principle of attention mechanism and realization of spatial attention mechanism and channel attention mechanism, and then designed the embedding of the spatial attention module and channel attention model in Xception structure based on Xception. Final, the network was optimized by jointing Triplet Loss and Softmax Loss in the network loss layer, to obtain a feature embedding space with high intra-class compactness and inter-class separation. This paper was experimented on two flower image data sets (Oxford 17 flowers and Oxford 102 flowers), the results show that the MLSAN, MLCAN, MLCSAN model proposed in this paper were 0.39%, 0.50%, and 0.72% higher on the Oxford 17 flowers data set​ and 0.52%, 0.63% and 0.85% higher on the data set Oxford 102 flowers data set.