Automatic Events and Concept Extraction Coordination in Videos using Intelligent Semantic Model
Abstract
A Tremendous increase in the use of video-based applications has revealed the need for extracting the content in videos. Video semantic event detection is essential for its video summarization and retrieval. Input video data and low-level features alone are not sufficient to fulfill the user’s needs that is, a deeper understanding of the content at the semantic level is required. The present manual techniques, which are inefficient, subjective and costly in time and limit the querying capabilities, are being used to bridge the gap between low-level representative features and high-level semantic content. Here, an Intelligent Video semantic content extraction system is proposed that allows the user to query and retrieve cluster objects, events, and concepts that are extracted automatically and provides a textual representation for the extracted frames. Domain ontology based fuzzy video semantic content model that uses spatial/temporal relations is used in event and concept definitions. This meta-ontology definition provides a wide-domain applicable rule construction standard that allows the user to construct ontology for wide domain. The proposed framework provides crisp video output eliminating unnecessary contents and explanation of video frames for physically challenged users which reduces the time consumption for semantic extraction in lengthy videos.
Keywords
Intelligent Video Semantic Content Model, Frameset Representation, Cluster Objects.