Fusing Hyperspectral Features and Image Deep Features for Classification and Retrieval of Meat

Taking the bacon as the experimental object to establish a method of classification and retrieval of meat food with the characteristics of spectral curve and image,and using ten 3×3 convolution layers,three 5×5 convolution layers,five 5×5 pool layers and two full concatenation layers model to feature the hyperspectral image,and the cross entropy was used as the optimization target. The multiple scattering correction and principal component analysis were used to preprocess and feature extraction,then the two features were fused,and the fusion features were classified by Support Vector Machine(SVM). The experimental results showed that only the image features obtained by CNN were considered as input data,and classified by SVM,the accuracy was 75%. However,when the features of hyperspectral curves were fused into the image features and classified by SVM,the accuracy could reach 99.2%. In addition,the Euclidean distance algorithm was used to return the ranked scores for the retrieved samples,which realized the retrieval of freshness levels. The experimental results showed the feasibility and effectiveness of the proposed method. In addition,the Euclidean distance of the retrieved sample and the standard sample feature vector is calculated,which realized the retrieval of freshness levels,which showed the feasibility and effectiveness of the method.