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Image annotation by hierarchical mapping of features

Image annotation by hierarchical mapping of features WWW 2007 / Poster Paper Topic: Semantic Web Image Annotation by Hierarchical Mapping of Features Qiankun Zhao {qzhao, Prasenjit Mitra C Lee Giles College of Information Sciences and Technology Pennsylvania State University, University Park, PA pmitra, giles}@ist.psu.edu ABSTRACT In this paper, we propose a novel approach of image annotation by constructing a hierarchical mapping between lowlevel visual features and text features utilizing the relations within and across both visual features and text features. Moreover, we propose a novel annotation strategy that maximizes both the accuracy and the diversity of the generated annotation by generalizing or specifying the annotation in the corresponding annotation hierarchy. Experiments with 4500 scienti c images from Royal Society of Chemistry journals show that the proposed annotation approach produces satisfactory results at di €erent levels of annotations. annotation strategy to maximize the diversity and accuracy of the predicted annotations based on the hierarchical mapping model. 2. HIERARCHICAL IMAGE CLUSTERING First, we propose to take into account the relations among features within the visual dimension and textual annotation to build two cluster hierarchies. An image usually contains multiple objects and the correlations among objects is expected to improve the annotation. We use hierarchical clustering because each http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Image annotation by hierarchical mapping of features

Association for Computing Machinery — May 8, 2007

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References (5)

Datasource
Association for Computing Machinery
Copyright
Copyright © 2007 by ACM Inc.
ISBN
978-1-59593-654-7
doi
10.1145/1242572.1242784
Publisher site
See Article on Publisher Site

Abstract

WWW 2007 / Poster Paper Topic: Semantic Web Image Annotation by Hierarchical Mapping of Features Qiankun Zhao {qzhao, Prasenjit Mitra C Lee Giles College of Information Sciences and Technology Pennsylvania State University, University Park, PA pmitra, giles}@ist.psu.edu ABSTRACT In this paper, we propose a novel approach of image annotation by constructing a hierarchical mapping between lowlevel visual features and text features utilizing the relations within and across both visual features and text features. Moreover, we propose a novel annotation strategy that maximizes both the accuracy and the diversity of the generated annotation by generalizing or specifying the annotation in the corresponding annotation hierarchy. Experiments with 4500 scienti c images from Royal Society of Chemistry journals show that the proposed annotation approach produces satisfactory results at di €erent levels of annotations. annotation strategy to maximize the diversity and accuracy of the predicted annotations based on the hierarchical mapping model. 2. HIERARCHICAL IMAGE CLUSTERING First, we propose to take into account the relations among features within the visual dimension and textual annotation to build two cluster hierarchies. An image usually contains multiple objects and the correlations among objects is expected to improve the annotation. We use hierarchical clustering because each

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