Guest editorial: Content-Based Multimedia Indexing

Multimedia indexing systems aim at providing easy, fast and accurate access to large multimedia repositories. Research in Content-Based Multimedia Indexing covers a wide spectrum of topics in content analysis, content description, content adaptation and content retrieval. Various tools and techniques from different fields such as data indexing, machine learning, pattern recognition, image analysis and human computer interaction have contributed to the success of multimedia systems. Although, there has been significant progress in the field, we still face situations when the system show limits in accuracy, generality and scalability. Hence, the goal of this special issue is to bring forward the recent advancements in content-based multimedia indexing. We received 48 submissions, but only accepted 17 (35.42 %), according to the review process that was very rigorous for this special issue and consisted of several rounds of review. The high number of submissions reflect the importance and timeliness of the research field on content-based multimedia indexing. The selected 17 papers in this special issue are high-class publications and cover a wide range of problems in content indexing of multimedia data. The first paper BVariability modelling for audio events detection in movies^ (DOI 10.1007/ s11042-014-2038-7), co-authored by Cedric Penet, Claire-Helene Demarty, Guillaume Gravier, and Patrick Gros, proposes to model the variability between the soundtracks of Hollywood movies using a factor analysis technique, which is then used to compensate the audio features. For that purpose they use multiple audio words sequences and contextual Bayesian networks. Multimed Tools Appl (2015) 74:1137–1142 DOI 10.1007/s11042-015-2474-z