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Improved Image Retrieval using Visual Sorting and Semi-Automatic Semantic Categorization of Images

Topics: Data Mining in Pattern Recognition; Feature Extraction; Neural Networks; Recognition and Indexing; Segmentation and Grouping

In Metadata Mining for Image Understanding - Volume 1: MMIU, 67-77, 2008, Funchal, Madeira, Portugal

Authors: Kai Uwe Barthel ; Sebastian Richter and Anuj Goyal

Affiliation: FHTW Berlin, Germany

ISBN: 978-989-8111-24-1

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Feature Extraction ; Features Extraction ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image and Video Analysis ; Informatics in Control, Automation and Robotics ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Segmentation and Grouping ; Sensor Networks ; Signal Processing ; Signal Processing, Sensors, Systems Modeling and Control ; Soft Computing ; Theory and Methods

Abstract: The increasing use of digital images has led to the growing problem of how to organize these images efficiently for search and retrieval. Interpretation of what we see in images is hard to characterize, and even more so to teacha machine such that any automated organization can be possible. Due to this, both keyword-based Internet image search systems and content-based image retrieval systems are not capable of searching images according to the human high-level semantics of images. In this paper we propose a new image search system using keyword annotations, low-level visual metadata and semantic inter-image relationships. The semantic relationships are earned exclusively from the human users’ interaction with the image search system. Our system can be used to search huge (web-based) image sets more efficiently. However, the most important advantage of the new system is that it can be used to generate semi-automatically semantic relationships between the images.

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Paper citation in several formats:
Barthel K., Richter S. and Goyal A. (2008). Improved Image Retrieval using Visual Sorting and Semi-Automatic Semantic Categorization of Images.In Metadata Mining for Image Understanding - Volume 1: MMIU, (VISIGRAPP 2008) ISBN 978-989-8111-24-1, pages 67-77. DOI: 10.5220/0002339400670077

@conference{mmiu08,
author={Kai Uwe Barthel and Sebastian Richter and Anuj Goyal},
title={Improved Image Retrieval using Visual Sorting and Semi-Automatic Semantic Categorization of Images},
booktitle={Metadata Mining for Image Understanding - Volume 1: MMIU, (VISIGRAPP 2008)},
year={2008},
pages={67-77},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002339400670077},
isbn={978-989-8111-24-1},
}

TY - CONF

JO - Metadata Mining for Image Understanding - Volume 1: MMIU, (VISIGRAPP 2008)
TI - Improved Image Retrieval using Visual Sorting and Semi-Automatic Semantic Categorization of Images
SN - 978-989-8111-24-1
AU - Barthel K.
AU - Richter S.
AU - Goyal A.
PY - 2008
SP - 67
EP - 77
DO - 10.5220/0002339400670077

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