Pattern Recognition, Knowledge Systems, and Signal Processing in Audio–Visual Content Retrieval

Dear Colleagues,

Multimedia content retrieval has been a challenge for many years. Although it is possible to manually annotate multimedia content in web databases, setting suitable keywords and metadata that can be captured by modern search engines responding to user queries, such content is still largely inaccessible through information retrieval systems. First of all, not all multimedia content of web databases is annotated. Second, the annotation keywords only have a small possibility to match the rich natural language content of user queries. The need for intelligent adapted algorithms and knowledge systems to capture the semantics of multimedia content and annotate it through high quality captions or summarizations, as well as the need to match semantics of user queries with multimedia content semantics, has grown very fast during recent years. Although multimedia content comprises of data (for instance, complex tables not well annotated to be accessible through queries), signals of any kind , images, and videos, it is the audio-visual content retrieval process that prevails the multimedia information retrieval problem

This Special Issue will be devoted to promote important state of the art developments in all pattern recognition research directions, including statistical, structural, and machine/deep learning, to extract higher order charactristics and semantics of audio–visual content in large scale web databases. Moreover, significant advancements of knowledge systems in matching semantic information of user queries and multimedia content are highly sought in this Special Issue. Finally, efficient signal processing techniques and transformations to extract informative features from audio–visual content, are indispensable for the success of the previously mentioned pattern recognition systems to provide usable higher order features and semantics.

High quality papers are pursued in the above research fields, with emphasis on integrated systems for audio–visual content retrieval. However, non systemic but high quality research in any of the above mentioned three major components of audio–visual content retrieval systems is very welcome, provided its performance is tested in large scale benchmarking web data bases.

Dr. Dimitrios Alexios Karras
Guest Editor

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• intelligent systems
• machine/deep learning
• expert systems
• fuzzy expert systems
• data sciences
• signal/image processing
• knowledge systems engineering
• multi agent systems
• graph theory
• formal systems modelling
• communications systems and protopcols