A User-Driven Ontology Guided Image Retrieval Model

The demand for image retrieval and browsing online is growing dramatically. There are hundreds of millions of images available on the current World Wide Web. For multimedia documents, the typical keyword-based retrieval methods assume that the user has an exact goal in mind in searching a set of images whereas users normally do not know what they want, or the user faces with a repository of images whose domain is less known and content is semantically complicated. In these cases it is difficult to decide what keywords to use for the query. In this paper, we propose a user-centered image retrieval method that is based on the current Web, keyword-based annotation structure, and combining Ontology guided knowledge representation and probabilistic ranking. A Web application for image retrieval using the proposed approach has been implemented. The model provides a recommendation subsystem to support and assist the user modifying the queries and reduces the user's cognitive load with the searching space. Experimental results show that the image retrieval recall and precision rates increased and therefore demonstrates the effectiveness of the model.

[1]  Franz Josef Radermacher,et al.  Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Judea Pearl) , 1990, SIAM Rev..

[2]  Asunción Gómez-Pérez,et al.  Six challenges for the Semantic Web , 2002, KR 2002.

[3]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[4]  Steffen L. Lauritzen,et al.  Bayesian updating in causal probabilistic networks by local computations , 1990 .

[5]  Shashi Kant,et al.  Statistical Reasoning - A Foundation for Semantic Web Reasoning , 2005, ISWC-URSW.

[6]  Vijay V. Raghavan,et al.  Content-Based Image Retrieval Systems - Guest Editors' Introduction , 1995, Computer.

[7]  Paulo Cesar G. da Costa,et al.  PR-OWL: A Bayesian Ontology Language for the Semantic Web , 2005, ISWC-URSW.

[8]  Remco C. Veltkamp,et al.  Content-based image retrieval systems: A survey , 2000 .

[9]  Fuhui Long,et al.  Fundamentals of Content-Based Image Retrieval , 2003 .

[10]  C. Zheng,et al.  ; 0 ; , 1951 .

[11]  Maristella Agosti,et al.  Information Retrieval and Hypertext , 1996, Information Retrieval and Hypertext.

[12]  Richard R. Muntz,et al.  Bayesian Network Models for Information Retrieval , 2000 .

[13]  Gustavo Carneiro,et al.  A database centric view of semantic image annotation and retrieval , 2005, SIGIR '05.

[14]  Yun Peng,et al.  A probabilistic extension to ontology language OWL , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[15]  Fabio Crestani,et al.  Soft computing in information retrieval: techniques and applications , 2000 .

[16]  Dieter Fensel,et al.  Towards the Semantic Web: Ontology-driven Knowledge Management , 2002 .

[17]  Brian McBride,et al.  Jena: Implementing the RDF Model and Syntax Specification , 2001, SemWeb.

[18]  James Ze Wang,et al.  Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  James Ze Wang,et al.  Studying digital imagery of ancient paintings by mixtures of stochastic models , 2004, IEEE Transactions on Image Processing.