Research On Video Intelligence Analysis Using Ontology

Public video intelligence is emerging as one kind of important resources for analyzing international relations and making strategic decisions. The rapid increase in the available amount of video data is creating a growing demand for efficient methods for understanding and managing it at the semantic level. One of the major challenges facing video semantic content analysis and the related applications is the so-called "the Semantic Gap" between the rich high-level semantics that users desire and the shallowness of the low-level features that the automatic algorithms can extract from the media. In this thesis, we systematically explore the problem of modeling and managing semantics of public video intelligence.Firstly, an architecture for video intelligence analysis is proposed. And video semantic content analysis is shown to be the core for video intelligence analysis. Secondly, a general framework for video semantic content analysis is presented based on ontology. Within this framework, methods of low-level semantic extraction and high-level semantic analysis are developed for video analysis. Finally, the above framework and methods are validated by designing and implementing a Video Intelligence Analysis Platform using Ontology (VIAPO). The main contributions of the thesis are as follows:We propose an architecture for video intelligence analysis, consisting of concept architecture and technique architecture. Concepts and the hierarchy of video analysis are defined within the concept architecture. And key techniques implementing video analysis are illustrated within the technique architecture.We suggest a novel unified framework for video semantic content analysis using ontology. Perception Concept, Meta Concept and High-level Concept are defined. Video semantic content are modeled with the above concepts and the relationships between them. Moreover, the construction of video intelligence knowledge base is proposed using ontology. And we propose a hierarchical approach for bridging the semantic gap combining domain knowledge.We address the methods of detecting Perception Concepts using machine learning techniques. In order to detect Perception Concepts, it is necessary to process high-dimensioned low level features automatically and discover meaningful patterns from the large amount of video data. Three methods are proposed to detect Perception Concepts comprehensively, which are composed of Audio Concepts detection based on Support Vector Machine,Visual Object Concept detection based on Conditional Random Field and Motion_Type Concepts detection based Gaussian Mixture Model.We develop an approach for high-level semantic analysis using ontology, which consists of concept detection in video intelligence and video intelligence retrieval. Meta Concept detection using ontology is proposed to overcome the drawbacks of traditional content-based methods. Based on Perception Concepts detection, Meta Concepts are detected combined with low-level features and context information. With the results of Meta Concepts detection, a novel method for high-level concept detection is proposed using Bayesian Net, which models the relations between low-level concepts and high-level concepts. With the demand of customizing video intelligence retrieval in mind, we propose a query description model based on Perception Concepts and video concepts composite PetriNet. The temporal relationships between the concepts interested by user are modeled by PetriNet, which supports the customization of video intelligence retrieval.We design and implement a Video Intelligence Analysis Platform using Ontology, which gives a sound support to the above framework and methods of video semantic content analysis.In conclusion, this thesis provides an in-depth investigation into the architecture of video intelligence analysis, the framework of video semantic content analysis and methods for bridging the semantic gap. This research is the foundation of video intelligence analysis, theoretically and practically. And it also improves the technology of video semantic content analysis.