FB-CNN: Feature Fusion-Based Bilinear CNN for Classification of Fruit Fly Image

Abstract

The high-resolution devices for image capturing and the high professional requirement for users, are very complex to extract features of the fruit fly image for classification. Therefore, a bilinear CNN model based on the mid-level and high-level feature fusion (FB-CNN) is proposed for classifying the fruit fly image. At the first step, the images of fruit fly are blurred by the Gaussian algorithm, and then the features of the fruit fly images are extracted automatically by using CNN. Afterward, the mid- and high-level features are selected to represent the local and global features, respectively. Then, they are jointly represented. When finished, the FB-CNN model was constructed to complete the task of image classification of the fruit fly. Finally, experiments data show that the FB-CNN model can effectively classify four kinds of fruit fly images. The accuracy, precision, recall, and F1 score in testing dataset are 95.00%, respectively.

Keywords

• Fruit fly images
• feature fusion
• convolution neural network
• image classification