Abstract
The objective of this special issue is to report on recent trends in digital and media technologies responding to the challenges of managing and accessing multimedia (images, audio, video, 3D/4D material, etc.). In a highly selective review procedure we accepted contributions describing recent work that aims at narrowing the large disparity between the low-level multimedia descriptors and the richness of subjectivity of semantics in user queries and human interpretations of audiovisual data. The articles in this special issue can be grouped into following categories: Multimedia Analysis (Section 1), Multimedia Ontologies and Data Integration (Section 2), and Social Media and Retrieval (Section 3).
Highlights
The articles in this special issue can be grouped into following categories: Multimedia Analysis (Section 1), Multimedia Ontologies and Data Integration (Section 2), and Social Media and Retrieval (Section 3)
The last paper uses high-level semantics to guide the low-level task of keyframe detection [9] in a reversal of traditional workflow
Diemert et al.’s work on mapping semantic scripts to the output of image processing algorithms aims at leveraging amateur video material in a professional production
Summary
The predominant mode of operation in multimedia analysis has traditionally been bottom-up, i.e., working out high-level semantic features from low-level and easy to compute characteristics of the multimedia representation. They predict that the approach of automated semantics extraction can be used to improve video summarisation, indexing and retrieval. This results in an ability to detect and locate significant novelty in the video stream In their concrete application, Yong et al first segmented wildlife video frames, extracted features and matched image blocks in order to automatically construct a co-occurrence matrix of labels that represent the semantic context of a scene. Yong et al first segmented wildlife video frames, extracted features and matched image blocks in order to automatically construct a co-occurrence matrix of labels that represent the semantic context of a scene They demonstrated that using this approach yields better keyframe extraction than using only the low-level features
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