ACM Transactions on Multimedia Computing, Communications, and Applications

In this article, we aim to improve the performance of visual tracking by combing different features of multiple modalities. The core idea is to use covariance matrices as feature descriptors and then use sparse coding to encode different features. The ...

State-of-the-art techniques for image and video classification take a bottom-up approach where local features are aggregated into a global final representation. Existing frameworks (i.e., bag of words or Fisher vectors) are specifically designed to ...

Action recognition has attracted increasing interest in computer vision due to its potential applications in many vision systems. One of the main challenges in action recognition is to extract powerful features from videos. Most existing approaches ...

Human pose estimation has an important impact on a wide range of applications, from human-computer interface to surveillance and content-based video retrieval. For human pose estimation, joint obstructions and overlapping upon human bodies result in ...

Video Question Answering (VideoQA) is the extension of image question answering (ImageQA) in the video domain. Methods are required to give the correct answer after analyzing the provided video and question in this task. Comparing to ImageQA, the most ...

Deep learning has revolutionized the field of computer vision and image processing. Its ability to extract the compact image representation has taken the person re-identification (re-id) problem to a new level. However, in most cases, researchers are ...

Person Re-identification is a very challenging task due to inter-class ambiguity caused by similar appearances, and large intra-class diversity caused by viewpoints, illuminations, and poses. To address these challenges, in this article, a graph ...

Cross-modality human behavior analysis has attracted much attention from both academia and industry. In this article, we focus on the cross-modality image-text retrieval problem for human behavior analysis, which can learn a common latent space for ...

Crowd counting is a popular topic with widespread applications. Currently, the biggest challenge to crowd counting is large-scale variation in objects. In this article, we focus on overcoming this challenge by proposing a novel Attentive Encoder-Decoder ...

Alzheimer’s disease is an incurable neurodegenerative disease primarily affecting the elderly population. Efficient automated techniques are needed for early diagnosis of Alzheimer’s. Many novel approaches are proposed by researchers for classification ...

Event extraction is one of the crucial tasks in biomedical text mining that aims to extract specific information concerning incidents embedded in the texts. In this article, we propose a deep learning framework that aims to identify the attributes (...

Computer-aided histopathological image analysis for cancer detection is a major research challenge in the medical domain. Automatic detection and classification of nuclei for cancer diagnosis impose a lot of challenges in developing state-of-the-art ...

Recent years have witnessed an emerging trend for improving disease treatment by forming multi-disciplinary medical teams. The collaboration among specialists from multiple medical domains has been shown to be significantly helpful for designing ...

Training gene expression data with supervised learning approaches can provide an alarm sign for early treatment of lung cancer to decrease death rates. However, the samples of gene features involve lots of noises in a realistic environment. In this ...

This article proposes a novel unsupervised approach (FigSum++) for automatic figure summarization in biomedical scientific articles using a multi-objective evolutionary algorithm. The problem is treated as an optimization problem where relevant ...

The early diagnosis of pulmonary cancer can significantly improve the survival rate of patients, where pulmonary nodules detection in computed tomography images plays an important role. In this article, we propose a novel pulmonary nodule detection ...

In a neural network, the weights act as parameters to determine the output(s) from a set of inputs. The weights are used to find the activation values of nodes of a layer from the values of the previous layer. Finding the ideal set of these weights for ...

Essential genes are considered to be the genes required to sustain life of different organisms. These genes encode proteins that maintain central metabolism, DNA replications, translation of genes, and basic cellular structure, and mediate the transport ...

Imbalanced data always has a serious impact on a predictive model, and most under-sampling techniques consume more time and suffer from loss of samples containing critical information during imbalanced data processing, especially in the biomedical ...