Journal Information
Advances in Multimedia (AM)
https://www.hindawi.com/journals/am/
Publisher:
Hindawi
ISSN:
1687-5680
Viewed:
83
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Call For Papers
Advances in Multimedia is a peer-reviewed, open access journal that publishes original research articles as well as review articles in all areas of multimedia.
Last updated by Dou Sun in 2017-04-29
Special Issues
Special Issue on Advances in Multimedia Security
Submission Date: 2017-05-26

Due to the consumedly growing amount of multimedia files nowadays, it seems that preventing the unauthorized operation such as accessing, copying, distribution, manipulation, and forgery of digital multimedia files (audio, images, and video) is undeniable and vital. Multimedia security requires answering the following questions: Who has created the multimedia file? When has the multimedia file been created? Who is given the right to access to the multimedia file? Who has modified the multimedia file? Which part of the multimedia file has been modified? Who is the owner of the multimedia file? This issue covers the theoretical and academic aspects of multimedia security as well as the industrial and commercial applications developed in this area. Potential topics include but are not limited to the following: Digital rights management systems and technical trends Multimedia compression technologies and standards Multimedia encryption Digital watermarking Digital steganography Multimedia security attacks Multimedia authentication Multimedia forensics Biometric features for user authentication Media sensor network Voice-over IP (VoIP) Security Multimedia key managements and emerging technology Project proposal and presentation Authors can submit their manuscripts through the Manuscript Tracking System at http://mts.hindawi.com/submit/journals/am/aims/. Manuscript Due Friday, 26 May 2017 First Round of Reviews Friday, 18 August 2017 Publication Date Friday, 13 October 2017
Last updated by Dou Sun in 2017-04-29
Special Issue on Learning Binary Representation for Computer Vision Applications
Submission Date: 2017-05-26

In the big data era, the volume of data has been dramatically enlarged than before. The traditional representation of data or feature learning algorithms may not work well or be computationally inapplicable for large-scale tasks, such as image retrieval and object recognition. It is desirable to develop new, efficient data representation or feature learning/indexing techniques, which can be easily performed with big data and achieve promising performance in the related tasks. In most recent years, the data-dependent hashing or compact binary code learning techniques have attracted broad research interests in computer vision, due to the high efficiency of storage and pairwise comparison with the Hamming distance. Benefiting from the nature of binary codes, these methods can well help perform various vision tasks (e.g., retrieval, classification), especially the ones with large-scale data. Recently, the binary representation learning techniques have been shown to achieve promising performance in various applications in computer vision, such as image retrieval, object recognition, and classifier training. This special issue will focus on the most recent progress on binary representation learning or data-dependent hashing methods for various visual tasks with large-scale data, such as content-based image/video classification, image retrieval/classification, image annotation, multimedia processing, and visual semantic analysis. This special issue will also target related fast feature extraction or representation learning techniques, which can well handle large-scale visual tasks. The primary objective of this special issue fosters focused attention on the latest research progress in this interesting area. The special issue seeks for original contribution of work, which addresses the challenges from the binary code learning and the related fast representation learning algorithms for large-scale data. Potential topics include but are not limited to the following: Novel locality sensitive hashing algorithms Large-scale indexing algorithms Learning based or data-dependent hashing/indexing methods Visual recognition (e.g., detection, categorization, indexing, matching, segmentation, and grouping) with binary code learning or hashing techniques Biometrics with binary representation learning Binary codes learning for visual classification/detection/retrieval/tracking Novel applications of hashing or binary representation learning Deep learning techniques for binary representation learning Fast feature extraction methods for visual data Fast learning algorithms for visual representation Big data, large scale methods Music/audio information retrieval Authors can submit their manuscripts through the Manuscript Tracking System at http://mts.hindawi.com/submit/journals/am/lbrc/. Manuscript Due Friday, 26 May 2017 First Round of Reviews Friday, 18 August 2017 Publication Date Friday, 13 October 2017
Last updated by Dou Sun in 2017-04-29
Special Issue on Social Annotations for Multimedia Resources
Submission Date: 2017-07-28

With the rapid development of the World Wide Web, social networking sites, wikis, and social tagging systems are becoming increasingly popular, and the social annotations give a new ground for identifying useful information for multimedia resources, where data mining techniques for social annotations play an important role. Along with the interactions between users and multimedia data, fruitful information can potentially be exploited by us to better model the multimedia resources via social computing techniques, such as personalization, mining user reviews, user profiling in social networks, and sentiment analysis for user opinion mining. Connecting social annotations and multimedia resources can not only enrich multimedia object modeling but also facilitate user-centered social computing in the big data era. Furthermore, these semantically rich annotations consolidate conventional multimedia techniques with social computing as a promising direction and offer opportunities for developing novel algorithms, methods, and tools for various multimedia objects. This special issue is intended for researchers and practitioners who are interested in issues that arise from using social annotations for multimedia resources. Potential topics include but are not limited to the following: Exploitation of the social annotations for multimedia data The identification of semantics underlying annotation data for user modeling and efficient algorithms for multimedia data management The application of social computing techniques in research fields related to (but not limited) the following: Social annotations for multimedia resources Semantic mining from social annotations Modeling multimedia resources by incorporating social annotations Multimedia applications in modality and multiple sources Social and collaborative information retrieval for multimedia data Personalized techniques for multimedia data Community extraction and exploitation for multimedia data Information propagation and user modeling in social networks User sentiment analysis and mining in multimedia big data Social review and opinion mining for multimedia data Mobile and ubiquitous computing for multimedia data Context-aware information access for multimedia data Trust, security, and privacy for multimedia data Learner modeling in multimedia educational data Semantic extraction and mining for multimedia data Efficient data mining and social computing in multimedia data Domain-specific user modeling and social computing applications in multimedia data Authors can submit their manuscripts through the Manuscript Tracking System at http://mts.hindawi.com/submit/journals/am/samr/. Manuscript Due Friday, 28 July 2017 First Round of Reviews Friday, 20 October 2017 Publication Date Friday, 15 December 2017
Last updated by Dou Sun in 2017-04-29
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