Call for Papers

IEEE Transactions on Neural Networks
Special Issue on Intelligent Multimedia Processing

Human communication is intrinsically multimodal. With the advances of technology, modern communication systems will also become more and more multimodal. Hence, multimedia technologies represent new ground for research interactions among a variety of media such as speech, audio, image, video, text and graphics. Future multimedia technologies will need to handle information with an increasing level of intelligence, i.e., automatic recognition and interpretation of multimodal signals. This is particularly emphasized in MPEG-7 which focuses on the 'multimedia content description interface'. The description shall be associated with the content itself to facilitate fast and effective searching for all the media. Specifically, the MPEG-7 research domain will cover techniques for content-based indexing and retrieval: pattern recognition, face detection/recognition, and fusion of multimodality.

Intelligent multimedia processing shares three fundamental goals with biological systems: a) Universal data processing engine for multimodal signals; b) Multimodality; and c) Unsupervised clustering and/or supervised learning by examples. Because of these features, neural networks are attractive candidates for intelligent multimedia processing and recent activity in the area is a proof of this fact. The main attribute of neural computing is its adaptive learning capability, which enables interpretations of possible variations of a same object or pattern, e.g., with respect to scale, orientation, and perspective. Moreover, they are able to accurately approximate unknown systems based on sparse sets of noisy data. Certain neural models also effectively incorporate statistical signal processing and optimization techniques. In addition, spatial/temporal neural structures and hierarchical models are promising for multirate, multiresolution multimedia processing. As a result, many successful applications of neural networks in intelligent multimedia processing, sometimes combined with fuzzy systems and evolutionary computing, have been reported.

The possible topics for the special issue include, but are not limited to, the following:

 * Neural networks (including BSS and ICA) and other computational intelligence models, learning paradigms, and architectures for multimedia processing.
 * Intelligent multimedia processing architectures.
 * Multimedia/multichannel data fusion.
 * Multimodal representation and information retrieval: Applications in hyperlinking of multimedia objects, query and search of multimedia
    information including intelligent web agents, 3D object representation and motion tracking, image sequence generation and animation.
 * Human-computer interaction and communications: face recognition, lip-reading analysis, facial expression and emotion categorization, interactive
    human-machine vision, speech recognition, speaker recognition, gesture analysis and recognition, auditory/visual scene analysis, and multimodal interaction.
 * Multimedia data analysis and visualization: texture, color, content, etc.
 * Intelligent network control of audio/video streams in multimedia networking applications.

Original, previously-unpublished research articles as well as state-of-the-art tutorial papers will be considered.

Authors should follow the IEEE TNN manuscript format described in the Information for Authors, which can be found on the inside back cover of any issue of TNN. Prospective authors are invited to submit papers to the website: http://eivind.imm.dtu.dk/tnn.

The following schedule will apply:

Guest Editors: