With the rapid development of deep learning, image semantic segmentation has made great
progress and become a hot topic in scene understanding of computer vision. In this paper …

A large amount of training image data is required for solving image classification problems
using deep learning (DL) networks. In this study, we aimed to train DL networks with …

Near-duplicate human motion classification presents significant challenges due to the subtle
differences and high similarity between actions. This paper introduces a posture estimation …

Recently, both single modality and cross modality near-duplicate image detection tasks
have received wide attention in the community of pattern recognition and computer vision …

Computer vision has always been concerned with near-duplicate image detection. Previous
approaches for detecting near duplicates highlighted the necessity to adequately explore …

Due to the great success of convolutional neural networks (CNNs) in the area of computer
vision, the existing methods tend to match the global or local CNN features between images …

Big data in medical diagnosis can provide abundant value for clinical diagnosis, decision
support and many other applications, but obtaining a large number of labeled medical data …

This paper presents a comparative study of near-duplicate image detection techniques in a
real-world use case scenario, where a document management company is commissioned to …

Instance-level object re-identification is a fundamental computer vision task, with
applications from image retrieval to intelligent monitoring and fraud detection. In this work …

Near-and duplicate image detection is a critical concern in the field of medical imaging.
Medical datasets often contain similar or duplicate images from various sources, which can …