Image representation methods based on deep convolutional neural networks (CNNs) have
achieved the state-of-the-art performance in various computer vision tasks, such as image …

Content-based image retrieval (CBIR), which is aimed to search images from a large size
image database based on visual contents of images in an efficient and accurate way as per …

Recent methods for deep metric learning have been focusing on designing different
contrastive loss functions between positive and negative pairs of samples so that the …

Deep feature embedding aims to learn discriminative features or feature embeddings for
image samples which can minimize their intra-class distance while maximizing their inter …

Extracting visual features for image retrieval by mimicking human cognition remains a
challenge. Opponent color and HSV color spaces can mimic human visual perception well …

Breast cancer is a deadly disease that affects millions of women worldwide. The
International Conference on Image Analysis and Recognition in 2018 presents the BreAst …

The local descriptors have been the backbone of most of the computer vision problems.
Most of the existing local descriptors are generated over the raw input images. In order to …

Feature fusion is an important skill to improve the performance in computer vision, the
difficult problem of feature fusion is how to learn the complementary properties of different …

Background There are a huge variety of data sources relevant to chemical, biological and
pharmacological research, but these data sources are highly siloed and cannot be queried …

The recent advancement in deep learning-based approaches vastly outperforms the
traditional image descriptors. Deep learning models, such as residual networks (ResNet) …