Rajkumar Kannan

Dance videos are interesting and semantics-intensive. At the same time, they are the complex type of videos compared to all other types such as sports, news and movie videos. In fact, dance video is the one which is less explored by the researchers across the globe. Dance videos exhibit rich semantics such as macro features and micro features and can be classified into several types. Hence, the conceptual modeling of the expressive semantics of the dance videos is very crucial and complex. This paper presents a generic Dance Video Semantics Model (DVSM) in order to represent the semantics of the dance videos at different granularity levels, identified by the components of the accompanying song. This model incorporates both syntactic and semantic features of the videos and introduces a new entity type called, Agent, to specify the micro features of the dance videos. The instantiations of the model are expressed as graphs. The model is implemented as a tool using J2SE and JMF to annotate the macro and micro features of the dance videos. Finally examples and evaluation results are provided to depict the effectiveness of the proposed dance video model. Keywords: Agents, Dance videos, Macro features, Micro features, Video annotation, Video semantics.

Dance video is one of the important types of narrative videos with semantic rich content. This paper proposes a new meta model, Dance Video Content Model (DVCM) to represent the expressive semantics of the dance videos at multiple granularity levels. The DVCM is designed based on the concepts such as video, shot, segment, event and object, which are the components of MPEG-7 MDS. This paper introduces a new relationship type called Temporal Semantic Relationship to infer the semantic relationships between the dance video objects. Inverted file based index is created to reduce the search time of the dance queries. The effectiveness of containment queries using precision and recall is depicted. Keywords: Dance Video Annotations, Effectiveness Metrics, Metamodeling, Temporal Semantic Relationships.

Recent advances in hardware sophistication related to graphics display, audio and video devices made available a large number of multimedia and hypermedia applications. These multimedia applications need to store and retrieve the different forms of media like text, hypertext, graphics, still images, animations, audio and video. Dance is one of the important cultural forms of a nation and dance video is one such multimedia types. Archiving and retrieving the required semantics from these dance media collections is a crucial and demanding multimedia application. This paper summarizes the difference dance video archival techniques and systems. Keywords: Multimedia, Culture Media, Metadata archival and retrieval systems, MPEG-7, XML.

This article considers a system for the semiautomatic annotation of an audio-visual media of dance domain, DMAR (Dance Media Annotation, authoring and Retrieval system). We present the architecture of the system, a manual annotation tool, a semiautomatic authoring tool and a search engine for the choreographers, dancers and students to demonstrate how the dance media can be semantically annotated and how this information can be used for the retrieval of the dance media objects. This article then outlines the underlying XML Schema based content description structures of DMAR and discusses the merits and demerits of our approach of evolving semantic network as the basis for the audio-visual content description. Further, this article proposes a quality metric, fidelity to evaluate the expressive power of the dance annotations. Finally, evaluation results are presented to depict the performance of the dance video queries in terms of precision and recall.

This paper presents a dance video content model (DVCM) to represent the semantics of the dance videos at multiple granularity levels. The DVCM is designed based on the concepts such as video, shot, segment, event and object, which are the components of MPEG- 7 MDS. This paper introduces a new relationship type called, Temporal Semantic Relationship to infer the semantic relationships between the dance video objects. Inverted file based index is created to reduce the search time of the dance video queries. An interactive query processor is designed using J2SE1.5 and JMF2.0 to perform the semantic search.