Journal Information
Multimedia Tools and Applications
Impact Factor:

Call For Papers
Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools, and case studies of multimedia applications. Experimental and survey articles are appropriate for the journal. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed.

Specific areas of interest include (but are not limited to):

Multimedia Tools:
Multimedia application enabling software
System software support for multimedia
Performance measurement tools for multimedia
Multimedia authoring tools
System hardware support for multimedia
Multimedia databases and retrieval
Web tools and applications
Multimedia Applications:
Prototype multimedia systems and platforms
Multimedia on information superhighways

Video on-demand
Interactive TV
Home shopping
Remote home care
Electronic album
Personalized electronic journals
Last updated by Dou Sun in 2017-04-13
Special Issues
Special Issue on Advances in Visual Analytics and Mining Visual data
Submission Date: 2017-05-26

Visual and multimedia analytics is an emerging field of research combining strengths from information analytics, geospatial analytics, scientific analytics, statistical analytics, knowledge discovery, data management & knowledge representation, presentation, production and dissemination, cognition, perception and interaction. Its goal is to gain insight into homogeneous, contradictory and incomplete data through the combination of automatic analysis methods with human background knowledge and intuition. While the scope of visual analytics is broad, one principle that has emerged over the years is the need for visual analytics systems to leverage computational methods in data mining, knowledge discovery, and machine learning for large-scale data analysis. In these systems, the human operator works alongside the computational processes in an integrated fashion - the computer can sift through large amounts of data and identify the relevant information, while the human interactively explores the reduced data space to discover trends and patterns and make informed decisions. The two components operate in coordination, allowing for a continuous and cooperative analytical loop. This special issue will publish papers that address how computational methods can be integrated into interactive visualization systems from a variety of perspectives. The dimensions listed below indicate the range of work that is relevant to the special issue. This special issue will be intended for researchers and practitioners who are interested in issues that arise from using visual analytics and mining visual data. Topics to be discussed in this issue include the following: 1.Mining Visual data - Information Extraction from Visual Data - Visual Analytics and Summarization Techniques - Visual Clustering Algorithms - Dimensionality Reduction and Topic Modeling - Transfer Learning from Visual Mining - Graphical models and Probabilistic models - Text Mining in Multimedia - Text Analytics in social medial - Visual Analytics in Social Media 2.Models, Theory, and Methods for Interactive Computational Visual Analytics - Mathematical foundations of data transformations - Data management and knowledge representation - Integration of multiple or disparate simulation models - Interaction, analytical discourse, and sensemaking - Analytic provenance and quantification and storage of interactions 3.Real-World Applications Using Interactive Computational Visual Analytics - Large-scale (real-world scale) data - High-dimensional data - Real-time data - Streaming data - Geospatial data 4.Evaluation of Interactive Computational Visual Analytics - Empirical and observational studies - User studies with general implications - Novel evaluation techniques
Last updated by Dou Sun in 2016-08-20
Special Issue on Interactive Multimedia in 5G Communications
Submission Date: 2017-06-30

This special issue will focus on major trends and challenges in this area, and will present work aimed to identify the researches for comprehensive and interactive multimedia applications and services with 5G. The topics of interest for this special issue include, but are not limited to: - Mobile entertainment system and service on 5G - Mobile multimedia broadcasting service and application - Multimedia cloud computing service on 5G - M2M (Machine-to-Machine) for multimedia technology on 5G - Multimedia data communication for 5G - Advanced mobile multimedia application and service - Multimedia resources in the cloud computing - Multimedia networking and QoS on 5G communication - Mobile audio/video streaming on 5G - Peer-to-peer media systems and services on 5G - Sensor networks on 5G - Video teleconferencing on 5G - Information visualization and interactive systems - Tools for media authoring, editing, browsing, and navigation with high speed communication - Signal processing including audio, video, image processing, and coding - Media meta-modeling techniques - Storage systems, databases, and retrieval for multimedia - Image, audio, video, genre clustering & classification - Video summarization and story generation - Mosaic, video panorama and background generation - 3D and depth information - Mobile and location-based media techniques on 5G - Social network services for mobile users
Last updated by Dou Sun in 2016-12-16
Special Issue on Security and privacy for Multimedia in the Internet of Things
Submission Date: 2017-06-30

Topics of interest include (but are not limited to): - Access control and authentication - IoT and Social Network security and privacy - Encryption of all types, including homomorphic encryption - Device and hardware security and privacy - Cybercrime detection technique and prevention; case studies in deep criminal network analysis - Denial of Service/ Distributed Denial of Service (DoS/DDoS) - Information Forensics - Data Leakage and Exfiltration - Intrusion Detection/Prevention Systems - Large scale simulations and experiments for security - Location based privacy ; privacy enhanced technologies - Risk Migitation, reduction and simulation - Large scale penetration testing and ethical hacking - Secure Machine-to-Machine communications in IoT - Identity management and standard - Data security, recovery and segregation - Secure integration of IoT and social networks
Last updated by Dou Sun in 2016-12-16
Special Issue on Multimedia-Aware IoT System Design
Submission Date: 2017-07-31

The topics of interest include, but are not limited to: - Multimedia-aware IoT system architecture - Multimedia-aware IoT communication stack and protocol - Multimedia streaming and encoding for IoT system - QoS, QoE, mobility, security technique for multimedia-aware IoT system - Multimedia-support embedded IoT device and forensics issues - Cloud computing and data analytics for multimedia-aware IoT system - Multimedia-aware IoT system modeling and simulation techniques - Multimedia-aware and various IoT service platform security - Prototypes, test-beds, field trials for multimedia-aware IoT system - Standardization and open source development for multimedia-aware IoT system
Last updated by Dou Sun in 2016-12-16
Special Issue on Semantic Approaches for Multimedia Retrieval Applications
Submission Date: 2017-07-31

Preferred topics in this issue include (but are not limited to): - Advanced Biometric Technologies - Advanced Cryptosystems for Multimedia Data - Advanced Vision Algorithms - Cognitive informatics for Multimedia - Cognitive vision systems - Context-aware Services - Deep Learning Algorithms for Multimedia - Intelligent Recommendation System - Intelligent Social Network Data Analysis - Knowledge Extraction from Multimedia Information - Pattern classification in Multimedia application - Recommendation System for Multimedia - Semantic Multimedia Retrieval - Semantic Analytics and Mining based on Multimedia - Semantic Security - Smart Access Control and Authentication - Smart Surveillance system - Spatio-temporal Relation based on Multimedia - Visual data processing and understanding - Visualization based on Sematic Approach
Last updated by Dou Sun in 2016-12-16
Special Issue on Data Preprocessing for Big Multimedia Data
Submission Date: 2017-08-01

Internet revolution has enabled us to acquire and gather massive amount of multimedia data relatively easily. However, a lot of issues appear in obtaining and processing such big multimedia data, such as data heterogeneity, data incompleteness (data missing), highdimensionality of data, etc. Moreover, many multimedia data sets simultaneously contain one or more of these issues. This makes the learning of big multimedia data difficult as most of the current techniques can only deal with homogeneous, complete, and moderatesized-dimensional data. Hence, there is a huge gap between the current machine learning techniques and the requirements of our real life. In this case, data preprocessing (such as data representation learning, dimensionality reduction, missing value imputation, etc) should be very interesting and challenging to relief such a gap. The goal of this proposal is to attract articles that cover existing aforementioned issues in data preprocessing of multimedia data. We would also like to accept successful applications of the new methods, including but not limited to data processing, analysis, and knowledge discovery of big multimedia data.
Last updated by Dou Sun in 2017-05-23
Special Issue on Frontiers in Multimedia Analytics: Emerging Media types, Technologies and Applications
Submission Date: 2017-08-01

Recent research in multimedia analytics is expanding the scope of multimedia data types as well as transforming the way we process and the field we apply these multimedia data. (1) Emerging media types: Collections of traditional multimedia data like documents, images, videos, and novel media types like social media data, network data, mobile data, sensor data, are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge. (2) Emerging techniques: Another tendency in multimedia analytics is the emergence of massive data. The techniques that work at smaller scales do not necessarily work, or work well, at such massive scale. New techniques are necessary that go far beyond classical feature extraction, clustering and indexing methods, aiming to find relational and semantic interpretations of the phenomena underlying the data. (3) Emerging applications: Many novel and promising multimedia analytic research directions are being proposed recently, e.g., image/video captioning, affection computing, multimedia storytelling, etc. Moreover, multimedia analytics is increasingly common in application fields like Internet commerce, healthcare, education, communications, augmented/virtual reality and elsewhere. This special issue will examine the frontier of utilizing novel multimedia data types by advanced machine learning and signal processing techniques, whether in a static database or streaming through a system. It will also discuss pitfalls in applying the state-of-the-art multimedia analytics techniques in emerging application fields. To summarize, the special issue characterizes three major lines of frontiers in multimedia analytics: (1) emerging media types, (2) emerging techniques, and (3) emerging applications.
Last updated by Dou Sun in 2017-05-23
Special Issue on Few-shot Learning for Multimedia Content Understanding
Submission Date: 2017-08-31

The multimedia analysis and machine learning communities have long attempted to build models for understanding real-world applications. Driven by the innovations in the architectures of deep convolutional neural network (CNN), tremendous improvements on object recognition and visual understanding have been witnessed in the past few years. However, it should be noticed that the success of current systems relies heavily on a lot of manually labeled noise-free training data, typically several thousand examples for each object class to be learned, like ImageNet. Although it is feasible to build learning systems this way for common categories, recognizing objects 'in the wild' is still very challenging. In reality, many objects follow a long-tailed distribution: they do not occur frequently enough to collect and label a large set of representative exemplars in contrast to common objects. For example, in some real-world applications, such as anomalous object detection in a video surveillance scenario, it is difficult to collect sufficient positive samples because they are 'anomalous' as defined, and fine-grained object recognition, annotating fine-grained labels requires expertise such that the labeling expense is prohibitively costly. The expensive labeling cost motivates the researchers to develop learning techniques that utilize only a few noise-free labeled data for model training. Recently, some few-shot learning, including the most challenging task zero-shot learning, approaches have been proposed to reduce the number of necessary labeled samples by transferring knowledge from related data sources. In the view of the promising results reported by these works, it is fully believed that the few-shot learning has strong potential to achieve comparable performance with the sufficient-shot learning techniques and significantly save the labeling efforts. There still remains some important problems. For example, a general theoretical framework for few-shot learning is not established, the generalized few-shot learning which recognizes common and uncommon objects simultaneously is not well investigated, and how to perform online few-shot learning is also an open issue. The primary goal of this special issue is to invite original contributions reporting the latest advances in fewshot learning for multimedia (e.g., text, video and audio) content understanding towards addressing these challenges, and to provide the opportunity for researchers and product developers to discuss the state-of-theart and trends of few-shot learning for building intelligent systems. The topics of interest include, but are not limited to: - Few-shot/zero-shot learning theory; - Novel machine learning techniques for few-shot/zero-shot learning; - Generalized few-shot/zero-shot learning; - Online few-shot/zero-shot learning; - Few-shot/zero-shot learning with deep CNN; - Few-shot/zero-shot learning with transfer learning; - Few-shot/zero-shot learning with noisy data; - Few-shot learning with actively data annotation (active learning); - Few-shot/zero-shot learning for fine-grained object recognition; - Few-shot/zero-shot learning for anomaly detection; - Few-shot/zero-shot learning for visual feature extraction; - Weakly supervised learning and its applications; - Attribute learning and its applications; - Leaning to hash and its applications; - Applications in object recognition and visual understanding with few-shot learning;
Last updated by Dou Sun in 2017-05-23
Special Issue on Content Based Multimedia Indexing
Submission Date: 2017-10-15

Multimedia indexing systems aim at providing user-friendly, fast and accurate access to large multimedia repositories. Various tools and techniques from different fields such as data indexing, machine learning, pattern recognition, and human computer interaction have contributed to the success of multimedia systems. In spite of significant progress in the field, content-based multimedia indexing systems still show limits in accuracy, generality and scalability. The goal of this special issue is to bring forward recent advancements in content-based multimedia indexing. In addition to multimedia and social media search and retrieval, we wish to highlight related and equally important issues that build on content-based indexing, such as multimedia content management, user interaction and visualization, media analytics, etc. The special issue will also feature contributions on application domains, e.g., deep learning for multimedia indexing, sparse data learning, cultural heritage and synergetic media production.
Last updated by Dou Sun in 2017-04-13
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