Feature selection (FS) for classification is crucial for large-scale images and bio-microarray data using machine learning. It is challenging to select informative features from high-dimensional data which generally contains many irrelevant and redundant features. These features often impede classifier performance and misdirect classification tasks. In this article, we present an efficient FS algorithm to improve classification accuracy by taking into account both the relevance of the features and the pairwise features correlation in regard to class labels. Based on conditional mutual information and entropy, a new supervised similarity measure is proposed. The supervised similarity measure is connected with feature redundancy minimization evaluation and then combined with feature relevance maximization evaluation. A new criterion max-relevance and min-supervised-redundancy (MRMSR) is introduced and theoretically proved for FS. The proposed MRMSR-based method is compared to seven existing FS approaches on several frequently studied public benchmark datasets. Experimental results demonstrate that the proposal is more effective at selecting informative features and results in better competitive classification performance.

Unmanned aerial vehicles (UAVs) are now widely used in many fields. Due to the randomness of UAV flight height and shooting angle, UAV images usually have the following characteristics: many small objects, large changes in object scale, and complex background. Therefore, object detection in UAV aerial images is a very challenging task. To address the challenges posed by these characteristics, this paper proposes a novel UAV image object detection method based on global feature aggregation and context feature extraction named the multi-scale feature information extraction and fusion network (MFEFNet). Specifically, first of all, to extract the feature information of objects more effectively from complex backgrounds, we propose an efficient spatial information extraction (SIEM) module, which combines residual connection to build long-distance feature dependencies and effectively extracts the most useful feature information by building contextual feature relations around objects. Secondly, to improve the feature fusion efficiency and reduce the burden brought by redundant feature fusion networks, we propose a global aggregation progressive feature fusion network (GAFN). This network adopts a three-level adaptive feature fusion method, which can adaptively fuse multi-scale features according to the importance of different feature layers and reduce unnecessary intermediate redundant features by utilizing the adaptive feature fusion module (AFFM). Furthermore, we use the MPDIoU loss function as the bounding-box regression loss function, which not only enhances model robustness to noise but also simplifies the calculation process and improves the final detection efficiency. Finally, the proposed MFEFNet was tested on VisDrone and UAVDT datasets, and the mAP0.5 value increased by 2.7% and 2.2%, respectively.