Information systems development and data modeling: conceptual and philosophical foundations
Information systems development and data modeling: conceptual and philosophical foundations
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Tractatus Logico Philosophicus (Routledge Classics) (Routledge Classics)
Tractatus Logico Philosophicus (Routledge Classics) (Routledge Classics)
Automatic video annotation using ontologies extended with visual information
Proceedings of the 13th annual ACM international conference on Multimedia
A new Mallows distance based metric for comparing clusterings
ICML '05 Proceedings of the 22nd international conference on Machine learning
Large-Scale Concept Ontology for Multimedia
IEEE MultiMedia
Semi-automatic, data-driven construction of multimedia ontologies
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Enhancing HSV histograms with achromatic points detection for video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Modal keywords, ontologies, and reasoning for video understanding
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
An ontology infrastructure for multimedia reasoning
VLBV'05 Proceedings of the 9th international conference on Visual Content Processing and Representation
Adding Semantics to Detectors for Video Retrieval
IEEE Transactions on Multimedia
IEEE Transactions on Circuits and Systems for Video Technology
Knowledge-assisted semantic video object detection
IEEE Transactions on Circuits and Systems for Video Technology
Linear Transition Detection as a Unified Shot Detection Approach
IEEE Transactions on Circuits and Systems for Video Technology
Learning Rules for Semantic Video Event Annotation
VISUAL '08 Proceedings of the 10th international conference on Visual Information Systems: Web-Based Visual Information Search and Management
Learning ontology rules for semantic video annotation
MS '08 Proceedings of the 2nd ACM workshop on Multimedia semantics
Ontology for Imagistic Domains: Combining Textual and Pictorial Primitives
ER '09 Proceedings of the ER 2009 Workshops (CoMoL, ETheCoM, FP-UML, MOST-ONISW, QoIS, RIGiM, SeCoGIS) on Advances in Conceptual Modeling - Challenging Perspectives
High-level concept annotation using ontology and probabilistic inference
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Ontological primitives for visual knowledge
SBIA'10 Proceedings of the 20th Brazilian conference on Advances in artificial intelligence
Event detection and recognition for semantic annotation of video
Multimedia Tools and Applications
| Hi-index | 0.00 |
In this paper, we present the dynamic pictorial ontology paradigm for video annotation. Ontologies are often used to describe a given domain for different goals, including description of multimedia data. In the case of video annotation, the visual knowledge cannot be described using only abstract concepts but is more effectively represented in a visual form. To this aim, we introduce visual concepts, elicited from the data set as the most representative prototypes that specialize abstract concepts. The ontology created is intrinsically dynamic since it must embrace the perceptual and visual experience during annotation. Thus visual concepts can change, adapting to the multimedia content analyzed. Motivation for this new ontology paradigm are discussed together with a proposal of a framework for ontology creation, maintenance, and automatic annotation of video. The creation and usage of dynamic pictorial ontologies have been tested for soccer domain exploiting low level perceptual features and higher level domain features.