Identification of Lycium barbarum pests based on zero-shot learning
In order to solve the problem of lack of effective zero-sample recognition and retrieval methods in agricul-tural field,a zero-sample learning-based retrieval and recognition method for Lycium barbarum pests was proposed in this study.Firstly,the deep structure features were obtained by deep matrix decomposition of the original data,and the charac-teristic representations of different modal data were obtained,and the hashing codes of each modality were generated.Then the linear constraint was introduced to the generated hashing code with the class attribute information to realize the knowl-edge transfer from the known class to the new class.Finally,the proposed model could avoid the quantization error caused by the continuous relaxation method and improve the retrieval precision by learning discrete hashing codes directly.The ex-perimental results on the three public datasets,2020 Ningxia Lycium barbarum pest image-text cross-modal retrieval data-set,Wiki,Pascal VOC,showed that the method proposed in this study was superior to the existing collective matrix factori-zation hashing(CMFH),latent semantic sparse hashing(LSSH),transferring supervised knowledge hashing(TSK),attribute hashing(AH),cross-modal attribute hashing(CMAH),cross-modal hashing with orthogonal projection(CHOP),and discrete asymmetric zero-shoot hashing(DAZSH).
万方数据
维普
