Semantic-Based Video Data Modeling for Scenery Documentary

Video data modeling is an important issue for content-based retrieval. In this paper, we propose a semantic-based four-layer video data model for scenery documentary and our discussions focus on the extracting and representation of semantics of video data. The support vector machine (SVM) is used to bridge the gap between low-level visual features and high-level semantic concepts. Semantic concept vectors are defined to represent the semantics of shot and scene. The retrieval based on our video data model can be achieved at low-level feature layer and high-level semantic concept layer. The results of retrieval at difference layers can be integrated to improve the performance of retrieval.