The Experts below are selected from a list of 120 Experts worldwide ranked by ideXlab platform

Jianqiang Wang - One of the best experts on this subject based on the ideXlab platform.

  • ISM - Image Content Annotation Based on Visual Features
    Eighth IEEE International Symposium on Multimedia (ISM'06), 2006
    Co-Authors: Lei Ye, Philip Ogunbona, Jianqiang Wang
    Abstract:

    Automatic image content annotation techniques attempt to explore structural visual features of images that describe image content and associate them with image semantics. In this paper, two types of Concept spaces, Atomic Concept and collective Concept spaces, are defined and the annotation problems in those spaces are formulated as feature classification and Bayesian inference, respectively. A scheme of image content annotation in this framework is presented and evaluated as an application of photo categorisation using MPEG-7 VCE2 dataset and its ground truth. The experimental results show a promising performance.

  • Image Content Annotation Based on Visual Features
    Eighth IEEE International Symposium on Multimedia (ISM'06), 2006
    Co-Authors: Lei Ye, Philip Ogunbona, Jianqiang Wang
    Abstract:

    Automatic image content annotation techniques attempt to explore structural visual features of images that describe image content and associate them with image semantics. In this paper, two types of Concept spaces, Atomic Concept and collective Concept spaces, are defined and the annotation problems in those spaces are formulated as feature classification and Bayesian inference, respectively. A scheme of image content annotation in this framework is presented and evaluated as an application of photo categorization using MPEG-7 VCE2 dataset and its ground truth. The experimental results show a promising performance

Lei Ye - One of the best experts on this subject based on the ideXlab platform.

  • ISM - Image Content Annotation Based on Visual Features
    Eighth IEEE International Symposium on Multimedia (ISM'06), 2006
    Co-Authors: Lei Ye, Philip Ogunbona, Jianqiang Wang
    Abstract:

    Automatic image content annotation techniques attempt to explore structural visual features of images that describe image content and associate them with image semantics. In this paper, two types of Concept spaces, Atomic Concept and collective Concept spaces, are defined and the annotation problems in those spaces are formulated as feature classification and Bayesian inference, respectively. A scheme of image content annotation in this framework is presented and evaluated as an application of photo categorisation using MPEG-7 VCE2 dataset and its ground truth. The experimental results show a promising performance.

  • Image Content Annotation Based on Visual Features
    Eighth IEEE International Symposium on Multimedia (ISM'06), 2006
    Co-Authors: Lei Ye, Philip Ogunbona, Jianqiang Wang
    Abstract:

    Automatic image content annotation techniques attempt to explore structural visual features of images that describe image content and associate them with image semantics. In this paper, two types of Concept spaces, Atomic Concept and collective Concept spaces, are defined and the annotation problems in those spaces are formulated as feature classification and Bayesian inference, respectively. A scheme of image content annotation in this framework is presented and evaluated as an application of photo categorization using MPEG-7 VCE2 dataset and its ground truth. The experimental results show a promising performance

H. Arenas - One of the best experts on this subject based on the ideXlab platform.

  • DEXA Workshops - Semantic matchmaking in geo service chains: reasoning with a location ontology
    2004
    Co-Authors: R. Lemmens, H. Arenas
    Abstract:

    The growing demand for the discovery of geo Web services as parts of service chains necessitates semantic matchmaking between service descriptions. We propose a method for describing and matching services based on a geo location ontology and we demonstrate its power and its limitations with a simple user scenario. In a test environment using protege and the racer ontology reasoner we show some powerful constructs of Description Logics that allow us to distinguish different types of matches. We underline the importance of matching Atomic Concept conditions versus aggregated conditions. The use of OWL and OWL enabled tools prove to facilitate a powerful matchmaking environment.

  • Semantic matchmaking in geo service chains: reasoning with a location ontology
    Proceedings. 15th International Workshop on Database and Expert Systems Applications 2004., 2004
    Co-Authors: R. Lemmens, H. Arenas
    Abstract:

    The growing demand for the discovery of geo Web services as parts of service chains necessitates semantic matchmaking between service descriptions. We propose a method for describing and matching services based on a geo location ontology and we demonstrate its power and its limitations with a simple user scenario. In a test environment using protege and the racer ontology reasoner we show some powerful constructs of Description Logics that allow us to distinguish different types of matches. We underline the importance of matching Atomic Concept conditions versus aggregated conditions. The use of OWL and OWL enabled tools prove to facilitate a powerful matchmaking environment.

Philip Ogunbona - One of the best experts on this subject based on the ideXlab platform.

  • ISM - Image Content Annotation Based on Visual Features
    Eighth IEEE International Symposium on Multimedia (ISM'06), 2006
    Co-Authors: Lei Ye, Philip Ogunbona, Jianqiang Wang
    Abstract:

    Automatic image content annotation techniques attempt to explore structural visual features of images that describe image content and associate them with image semantics. In this paper, two types of Concept spaces, Atomic Concept and collective Concept spaces, are defined and the annotation problems in those spaces are formulated as feature classification and Bayesian inference, respectively. A scheme of image content annotation in this framework is presented and evaluated as an application of photo categorisation using MPEG-7 VCE2 dataset and its ground truth. The experimental results show a promising performance.

  • Image Content Annotation Based on Visual Features
    Eighth IEEE International Symposium on Multimedia (ISM'06), 2006
    Co-Authors: Lei Ye, Philip Ogunbona, Jianqiang Wang
    Abstract:

    Automatic image content annotation techniques attempt to explore structural visual features of images that describe image content and associate them with image semantics. In this paper, two types of Concept spaces, Atomic Concept and collective Concept spaces, are defined and the annotation problems in those spaces are formulated as feature classification and Bayesian inference, respectively. A scheme of image content annotation in this framework is presented and evaluated as an application of photo categorization using MPEG-7 VCE2 dataset and its ground truth. The experimental results show a promising performance

Michael Zakharyaschev - One of the best experts on this subject based on the ideXlab platform.

  • A Cookbook for Temporal Conceptual Data Modelling with Description Logics
    ACM Transactions on Computational Logic, 2014
    Co-Authors: Alessandro Artale, Roman Kontchakov, Vladislav Ryzhikov, Michael Zakharyaschev
    Abstract:

    We design temporal description logics (TDLs) suitable for reasoning about temporal Conceptual data models and investigate their computational complexity. Our formalisms are based on DL-Lite logics with three types of Concept inclusions (ranging from Atomic Concept inclusions and disjointness to the full Booleans), as well as cardinality constraints and role inclusions. The logics are interpreted over the Cartesian products of object domains and the flow of time (Z, og S pace and PS pace . These positive results are obtained by reduction to various clausal fragments of propositional temporal logic, which opens a way to employ propositional or first-order temporal provers for reasoning about temporal data models.

  • A Cookbook for Temporal Conceptual Data Modelling with Description Logics
    arXiv: Logic in Computer Science, 2012
    Co-Authors: Alessandro Artale, Roman Kontchakov, Vladislav Ryzhikov, Michael Zakharyaschev
    Abstract:

    We design temporal description logics suitable for reasoning about temporal Conceptual data models and investigate their computational complexity. Our formalisms are based on DL-Lite logics with three types of Concept inclusions (ranging from Atomic Concept inclusions and disjointness to the full Booleans), as well as cardinality constraints and role inclusions. In the temporal dimension, they capture future and past temporal operators on Concepts, flexible and rigid roles, the operators `always' and `some time' on roles, data assertions for particular moments of time and global Concept inclusions. The logics are interpreted over the Cartesian products of object domains and the flow of time (Z,

  • a cookbook for temporal Conceptual data modelling with description logics
    arXiv: Logic in Computer Science, 2012
    Co-Authors: Alessandro Artale, Roman Kontchakov, Vladislav Ryzhikov, Michael Zakharyaschev
    Abstract:

    We design temporal description logics suitable for reasoning about temporal Conceptual data models and investigate their computational complexity. Our formalisms are based on DL-Lite logics with three types of Concept inclusions (ranging from Atomic Concept inclusions and disjointness to the full Booleans), as well as cardinality constraints and role inclusions. In the temporal dimension, they capture future and past temporal operators on Concepts, flexible and rigid roles, the operators `always' and `some time' on roles, data assertions for particular moments of time and global Concept inclusions. The logics are interpreted over the Cartesian products of object domains and the flow of time (Z,<), satisfying the constant domain assumption. We prove that the most expressive of our temporal description logics (which can capture lifespan cardinalities and either qualitative or quantitative evolution constraints) turn out to be undecidable. However, by omitting some of the temporal operators on Concepts/roles or by restricting the form of Concept inclusions we obtain logics whose complexity ranges between PSpace and NLogSpace. These positive results were obtained by reduction to various clausal fragments of propositional temporal logic, which opens a way to employ propositional or first-order temporal provers for reasoning about temporal data models.