Statistical Characteristics-Based Multi-Scale Image Feature Extraction

Abstract

Convolutional Neural Networks (CNNs) have improved image feature extraction ability at various scales. However, it fails to match the input features with different importance or complexity into the appropriate feature extraction branches more effectively. To solve this problem, we propose a novel statistical characteristics-based Multi-Scale Image Feature Extraction (SC-MSIFE) scheme for CNN, which adaptively matches batch image feature subsets into the appropriate feature extraction branches. We calculate and aggregate the gray distribution statistics of features to characterize the complexity, importance and interdependencies of batch image feature subsets, respectively. Then, we reorder the batch image feature subsets according to the gained information. Finally, we match the complex and significant batch image feature subsets into multi-scale feature extraction branches, while inputting the batch image feature subsets with simple and unimportant features into fewer-scale feature extraction branches. Extensive simulation results demonstrate the effectiveness of our proposed approach compared to baselines in terms of improving classification accuracy.