Track topics on Twitter Track topics that are important to you
Automatic image annotation not only has the efficiency of text-based image retrieval but also achieves the accuracy of content-based image retrieval. Users of annotated images can locate images they want to search by providing keywords. Currently most automatic image annotation algorithms do not consider the relative importance of each region in the image, and some algorithms extract the image features as a whole. This makes it difficult for annotation words to reflect salient versus non-salient areas of the image. Users searching for images are usually only interested in the salient areas. We propose an algorithm that integrates a visual attention mechanism with image annotation. A preprocessing step divides the image into two parts, the salient regions and everything else, and the annotation step places a greater weight on the salient region. When the image is annotated, words relating to the salient region are given first. The support vector machine uses particle swarm optimization to annotate the images automatically. Experimental results show the effectiveness of the proposed algorithm.
This article was published in the following journal.
Name: PloS one
We present here for the first time a fast and reliable automatic algorithm based on artificial neural networks for the anomaly detection of a thermoluminescence dosemeter (TLD) glow curves (GCs), and ...
Recent progress has been made in using attention based encoder-decoder framework for image and video captioning. Most existing decoders apply the attention mechanism to every generated word including ...
In this study, we compared deep learning (DL) with support vector machine (SVM), both of which use three-dimensional optical coherence tomography (3D-OCT) images for detecting epiretinal membrane (ERM...
Electrocardiogram (ECG) classification is an important process in identifying arrhythmia, and neural network models have been widely used in this field. However, these models are often disrupted by he...
The prediction of peptide-protein or protein-protein interactions (PPI) is a challenging task, especially if amino acid sequences are the only information available. Machine learning methods allow us ...
This is a randomized controlled prospective study which assigned patient to receive manual CPR or automatic CPR machine use. The quality and efficacy between manual CPR and machine CPR wil...
This project studies memory and attention in healthy children aged from 5 to 17 years. The processes investigated are short term memory (auditory and visual) and attention. To characteriz...
The objective of this pilot work is to determine the role of central and peripheral visions in explicit attention processes (saccade planning) in the case of visual impairment.
The aim of this study is to measure visual acuity and contrast sensitivity using a novel reaction-time based visual field device Ocusweep compared to widely used methods. Addition to that...
The primary objective of this study is to examine the role of machine learning and computer aided diagnostics in automatic polyp detection and to determine whether a combination of colonos...
SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.
Apparatus that provides mechanical circulatory support during open-heart surgery, by passing the heart to facilitate surgery on the organ. The basic function of the machine is to oxygenate the body's venous supply of blood and then pump it back into the arterial system. The machine also provides intracardiac suction, filtration, and temperature control. Some of the more important components of these machines include pumps, oxygenators, temperature regulators, and filters. (UMDNS, 1999)
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.
Abnormally rapid heartbeats originating from one or more automatic foci (nonsinus pacemakers) in the HEART ATRIUM but away from the SINOATRIAL NODE. Unlike the reentry mechanism, automatic tachycardia speeds up and slows down gradually. The episode is characterized by a HEART RATE between 135 to less than 200 beats per minute and lasting 30 seconds or longer.