Content‐based image retrieval (CBIR) systems require users to query images by their low‐
level visual content; this not only makes it hard for users to formulate queries, but also can …

Abstract Language data available through social media provide opportunities to study
people at an unprecedented scale. However, little guidance is available to psychologists …

Abstract We introduce Mixture-based Feature Space Learning (MixtFSL) for obtaining a rich
and robust feature representation in the context of few-shot image classification. Previous …

Automatic analysis of biomedical time series such as electroencephalogram (EEG) and
electrocardiographic (ECG) signals has attracted great interest in the community of …

A number of pattern recognition tasks,\textit {eg}, face verification, can be boiled down to
classification or clustering of unit length directional feature vectors whose distance can be …

Fault diagnosis has played a vital role in industry to prevent operation hazards and failures.
To overcome the limitation of conventional diagnosis approaches, which misclassify new …

Deep convolutional neural network (DCNN) is a powerful method of learning image features
with more discriminative and has been studied deeply and applied widely in the field of …

Adapting in-vehicle systems (eg, advanced driver assistance systems and in-vehicle
information systems) to individual drivers' workload can enhance both safety and …

In this paper, we consider the problem of remote sensing image classification, in which
feature extraction and feature coding are critical steps. Various feature extraction methods …

Early diagnosis and assessment of fatal diseases and acute infections on chest X-ray (CXR)
imaging may have important therapeutic implications and reduce mortality. In fact, many …