An accurate image retrieval technique is required due to the rapidly increasing number of
images. It is important to implement image annotation techniques that are fast, simple, and …

Recently, much attention has been given to image annotation due to the massive increase in
image data volume. One of the image retrieval methods which guarantees the retrieval of …

Web contains a vast amount of data, which are accumulated, studied, and utilized by a huge
number of users on a daily basis. A substantial amount of data on the Web is available in an …

Supervised machine learning methods for image analysis require large amounts of labelled
training data to solve computer vision problems. The recent rise of deep learning algorithms …

Deep convolution neural networks (CNNs) have demonstrated advanced performance on
single-label image classification, and various progress also has been made to apply CNN …

Multi-label image classification is a fundamental but challenging task towards general visual
understanding. Existing methods found the region-level cues (eg, features from RoIs) can …

We present a novel unsupervised domain adaptation method for semantic segmentation that
generalizes a model trained with source images and corresponding ground-truth labels to a …

Graph-based sparse feature selection plays an important role in semi-supervised feature
selection. However, traditional graph-based semi-supervised sparse feature selection …

When an attacker wants to falsify an image, in most of cases she/he will perform a JPEG
recompression. Different techniques have been developed based on diverse theoretical …

Despite broad investigation in content-based image retrieval (CBIR), issue to lessen the
semantic gap between high-level semantics and local attributes of the image is still an …