A hybrid approach for image retrieval with ontological content-based indexing

Authors:
Oleg Starostenko;Alberto Chávez-Aragón;J. Alfredo Sánchez;Yulia Ostróvskaya
Affiliations:
Computer Science Department, Universidad de las Américas, Puebla, Cholula, Puebla, Mexico;Computer Science Department, Universidad de las Américas, Puebla, Cholula, Puebla, Mexico;Computer Science Department, Universidad de las Américas, Puebla, Cholula, Puebla, Mexico;Computer Science Department, Universidad de las Américas, Puebla, Cholula, Puebla, Mexico
Venue:
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2005

Citing 7
Cited 0

A translation approach to portable ontology specifications

Knowledge Acquisition - Special issue: Current issues in knowledge modeling
SUSAN—A New Approach to Low Level Image Processing

International Journal of Computer Vision
Principles of visual information retrieval

Principles of visual information retrieval
The design and implementation of the redland RDF application framework

Proceedings of the 10th international conference on World Wide Web
Ontologies: a silver bullet for knowledge management and electronic commerce

Ontologies: a silver bullet for knowledge management and electronic commerce
The Semantic Web And Its Languages

IEEE Intelligent Systems
PicToSeek: combining color and shape invariant features for image retrieval

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel approach for image retrieval from digital collections. Specifically, we describe IRONS (Image Retrieval with Ontological Descriptions of Shapes), a system based on the application of several novel algorithms that combine low-level image analysis techniques with automatic shape extraction and indexing. In order to speed up preprocessing, we have proposed and implemented the convex regions algorithm and discrete curve evolution approach. The image indexing module of IRONS is addressed using two proposed algorithms: the tangent space and the two-segment turning function for shapes representation invariant to rotation and scale. Another goal of the proposed method is the integration of user-oriented descriptions, which leads to more complete retrieval by accelerating the convergence to the expected result. For the definition of image semantics, ontology annotation of sub-regions has been used.