Image Recognition Based on Compressive Imaging and Optimal Feature Selection

Abstract

The measurement matrix in compressive imaging controls the crucial feature information for high performance recognition. In this study, a deterministic orthogonal measurement matrix design method using the discrete cosine transform and a compressive feature selection scheme are proposed to implement high-end computational optics for imaging. The selection scheme systematically evaluates the recognition importance for the frequency features, combined with a scaling of the contribution of the various coefficients used to produce a base matrix for the new group of measuring patterns, which ensures the minimal recognition difference for each individual order of frequency filters and combining a relatively complex expression to quickly find the best quantization values. The model parameters are gradually adjusted and eventually converge to the best result through training with a large number of pre-determined samples from the dataset and backpropagating the feature selection loss along with the recognition loss, and the data processing capabilities can be enhanced because the measurement matrix is a priori information for the recognition phase. The systematic ability of the proposed technique was verified through simulations and experiments on two standard datasets: MNIST and CIFAR-10. The results show that the proposed method outperforms stateof-the-art methods in terms of both the model complexity and classification accuracy, which indicates that our study provides a new practical solution for compressive imaging recognition.

Underwater target detection algorithm based on improved YOLOv4 with SemiDSConv and FIoU loss function

“…YOLOv4 includes three types of loss functions: confidence loss, category loss, and localization loss (also called the loss of bounding box coordinates). Different from YOLOv3, YOLOv4 substitutes Complete-IoU (CIoU) (Zheng et al, 2021) loss for cross entropy loss in YOLOv3 as the localization loss function and obtains better convergence speed and accuracy (Jiao et al, 2022). The CIoU loss was improved from Distance-IoU (DIoU) (Zheng et al, 2020) loss.…”

Time Series Feature Selection Method Based on Mutual Information

“…Its main function is to reduce computational complexity, avoid the "curse of dimensionality" problem [1], reduce training time, and improve the performance of the predictor [2]. Therefore, how to effectively extract system features is one of the key issues in the field of time series analysis [3], which has been widely used in the following fields: image recognition [4,5], natural language processing [6,7], data mining [8,9], fault diagnosis [10][11][12], remaining useful life prediction [13,14], microbes classification [15], fatigue detection [16], image classification [17], intrusion detection [18][19][20][21], etc.…”