Showing papers by "Gaurav Harit published in 2006"
18 Dec 2006
TL;DR: This work presents a novel approach for defining video domain concepts in an ontology using properties that can be observed from the media and proposes the use of Bayesian network as the reasoning mechanism for doing inferencing tasks in the presence of uncertainty.
Abstract: To enable seamless integration of video information on the semantic web, we require that the knowledge of a video domain be formally specified in an ontology. We present a novel approach for defining video domain concepts in an ontology using properties that can be observed from the media. We use the ontology specified knowledge for recognizing concepts relevant to a video scene by making observations for the media properties of concepts as well as making inferences from other ontological concept definitions and relations. For this purpose we introduce new language constructs to OWL (Web Ontology Language), which are used to specify the inherently uncertain nature of media observations. The new constructs also allow additional semantics concerned with the association of media properties with concepts. We propose the use of Bayesian network as the reasoning mechanism for doing inferencing tasks in the presence of uncertainty. The video is annotated with the relevant concepts defined in the ontology. These conceptual annotations are used to create hyperlinks in the video collection.
14 citations
TL;DR: This work proposes a novel clustering strategy, tailored towards the specific requirements of clustering in video data, that takes care of many of the problems with traditional clustering schemes applied to the heterogeneous feature space of video.
7 citations
13 Jan 2006
TL;DR: In this article, the authors propose a perceptual prominence-based approach to the spatio-temporal domain of video, which is applied on blob tracks and makes use of a specified spatiotemporal coherence model.
Abstract: We propose an empirical computational model for generating an interpretation of a video shot based on our proposed principle of perceptual prominence. The principle of perceptual prominence captures the key aspects of mise-en-scene required for interpreting a video scene. We present a novel approach for applying perceptual grouping principles to the spatio-temporal domain of video. Our spatio-temporal perceptual grouping scheme, applied on blob tracks, makes use of a specified spatio-temporal coherence model. A high level semantic interpretation of scenes is done using the mise-en-scene features and the perceptual prominence computed for the perceptual clusters.
3 citations
Journal Article•
TL;DR: An empirical computational model is proposed for generating an interpretation of a video shot based on the proposed principle of perceptual prominence, which captures the key aspects of mise-en-scene required for interpreting a video scene.
Abstract: We propose an empirical computational model for generating an interpretation of a video shot based on our proposed principle of perceptual prominence. The principle of perceptual prominence captures the key aspects of mise-en-scene required for interpreting a video scene. We present a novel approach for applying perceptual grouping principles to the spatio-temporal domain of video. Our spatio-temporal perceptual grouping scheme, applied on blob tracks, makes use of a specified spatio-temporal coherence model. A high level semantic interpretation of scenes is done using the mise-en-scene features and the perceptual prominence computed for the perceptual clusters.
1 citations