Image Target Recognition Based on Multiregional Features under Hybrid Attention Mechanism

Abstract

The growing volume of image data calls for better real-time performance of image feature extraction algorithms. To enhance the recognition accuracy of image targets, it is significant to build a more scientific deep learning network. Multimodal cross convolution or densely connected blocks have been introduced to classic deep learning networks, aiming to promote the recognition of image targets. However, these attempts fail to satisfactorily extract detailed features from the original image. To solve the problem, this paper explores the image target recognition based on multiregional features under hybrid attention mechanism. Specifically, a convolutional neural network (CNN) was established for extracting multiregional features based on the loss function of local feature aggregation. The model consists of three independent CNN modules, which are responsible for extracting the global multiregional features and the local features of different regions. Next, the channel domain attention mechanism and spatial domain attention mechanism were embedded in the proposed CNN, such that the model can recognize targets more accurately, without increasing the computing load. Finally, the proposed network was proved effective through the training and testing on a self-developed sample set of surveillance video images.