[Paper Review] Semantic Modeling and Retrieval of Dance Video Annotations

Rajkumar Kannan, Ramadoss Balakrishnan|arXiv (Cornell University)|Jan 4, 2010

Video Analysis and Summarization4 references3 citations

TL;DR

This paper proposes the Dance Video Content Model (DVCM), a semantic meta-model for representing dance video content across multiple granularities using video, shot, segment, event, and object concepts from MPEG-7 MDS. It introduces Temporal Semantic Relationships to infer semantic links between dance elements and employs an inverted file index to accelerate query retrieval, demonstrating improved precision and recall in containment queries for dance video annotations.

ABSTRACT

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.

Motivation & Objective

• To address the challenge of representing rich semantic content in narrative dance videos using structured modeling.
• To model dance semantics at multiple granularities—video, shot, segment, event, and object—using MPEG-7 MDS concepts.
• To define a novel relationship type, Temporal Semantic Relationship, to capture semantic dependencies between dance video objects.
• To reduce query response time in dance video retrieval through an inverted file-based indexing mechanism.
• To evaluate retrieval effectiveness using precision and recall metrics for containment queries.

Proposed method

• Design of the Dance Video Content Model (DVCM) as a meta-model integrating MPEG-7 MDS components for semantic representation.
• Definition of Temporal Semantic Relationships to model dynamic semantic links between dance objects across time.
• Construction of an inverted file index over annotated video elements to enable fast semantic query processing.
• Mapping of dance video annotations into the DVCM structure to support semantic reasoning and retrieval.
• Use of containment queries to test retrieval performance, with precision and recall as evaluation metrics.
• Integration of the DVCM with standard multimedia metadata standards to ensure interoperability and extensibility.

Experimental results

Research questions

• RQ1 How can dance video semantics be effectively modeled across multiple levels of granularity?
• RQ2 What type of semantic relationship best captures the dynamic, time-dependent nature of dance actions?
• RQ3 How can indexing structures be optimized to accelerate semantic queries in dance video retrieval?
• RQ4 To what extent does the proposed model improve retrieval precision and recall for semantic dance queries?
• RQ5 Can the DVCM support expressive, content-rich queries while maintaining efficient query response times?

Key findings

• The proposed DVCM enables structured representation of dance video semantics at multiple granularities, from shots to events.
• Temporal Semantic Relationships successfully model dynamic semantic dependencies between dance objects, enhancing retrieval expressiveness.
• The inverted file index significantly reduces query response time, improving scalability for large-scale dance video collections.
• Containment queries achieved high precision and recall, demonstrating the model’s effectiveness in semantic retrieval tasks.
• The integration of MPEG-7 MDS concepts with custom semantic relationships enhances interoperability and extensibility of the model.
• The approach provides a foundation for advanced semantic search in expressive, narrative video domains like dance.