Guest Editorial: Content-based Multimedia Indexing

Content-Based 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, and Human Computer Interaction have contributed to the success of multimedia systems. Although, there has been a significant progress in the field, we still face situations when the system shows 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. The papers included contain significant original new information and ideas. For this special issue, we received a total of 14 submissions, of which 8 were accepted after a rigorous review process that consisted of several rounds of review. The 8 selected papers cover a wide range of problems in content indexing of multimedia data, including image, audio, video and multi-modal content. The first paper BA comparative study for multiple visual concepts detection in images and videos^ (DOI 10.1007/s11042-015-2730-2), co-authored by Abdelkader Hamadi, Philippe Mulhem and Georges Quénot, describes a comparative study and new methods for multiconcept detection in images and videos. The authors propose original fusion algorithms of oneconcept detectors and propose a new stacking scheme for them. Large evaluations on the Multimed Tools Appl (2016) 75:8969–8972 DOI 10.1007/s11042-016-3683-9