Exploiting ontologies for automatic image annotation
- 15 August 2005
- proceedings article
- Published by Association for Computing Machinery (ACM)
- p. 552-558
- https://doi.org/10.1145/1076034.1076128
Abstract
Automatic image annotation is the task of automatically assigning words to an image that describe the content of the image. Machine learning approaches have been explored to model the association between words and images from an annotated set of images and generate annotations for a test image. The paper proposes methods to use a hierarchy defined on the annotation words derived from a text ontology to improve automatic image annotation and retrieval. Specifically, the hierarchy is used in the context of generating a visual vocabulary for representing images and as a framework for the proposed hierarchical classification approach for automatic image annotation. The effect of using the hierarchy in generating the visual vocabulary is demonstrated by improvements in the annotation performance of translation models. In addition to performance improvements, hierarchical classification approaches yield well to constructing multimedia ontologies.Keywords
This publication has 10 references indexed in Scilit:
- Regularizing translation models for better automatic image annotationPublished by Association for Computing Machinery (ACM) ,2004
- Effective automatic image annotation via a coherent language model and active learningPublished by Association for Computing Machinery (ACM) ,2004
- Automatic linguistic indexing of pictures by a statistical modeling approachPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Modeling annotated dataPublished by Association for Computing Machinery (ACM) ,2003
- Automatic image annotation and retrieval using cross-media relevance modelsPublished by Association for Computing Machinery (ACM) ,2003
- Learning the semantics of words and picturesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Lexical chains for question answeringPublished by Association for Computational Linguistics (ACL) ,2002
- Normalized cuts and image segmentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2000
- Experiments on using semantic distances between words in image caption retrievalPublished by Association for Computing Machinery (ACM) ,1996
- Maximum Likelihood from Incomplete Data Via the EM AlgorithmJournal of the Royal Statistical Society Series B: Statistical Methodology, 1977