Towards large-scale multimedia retrieval enriched by knowledge about human interpretation
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- 作者：Kimiaki Shirahama ; Marcin Grzegorzek
- 关键词：Large ; scale multimedia retrieval ; Human ; machine cooperation ; Machine ; based methods ; Human ; based methods
- 刊名：Multimedia Tools and Applications
- 出版年：2016
- 出版时间：January 2016
- 年：2016
- 卷：75
- 期：1
- 页码：297-331
- 全文大小：1,180 KB
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Marcin Grzegorzek (1)
1. Pattern Recognition Group, University of Siegen, Hoelderlinstrasse 3, 57076, Siegen, Germany - 刊物类别：Computer Science
- 刊物主题：Multimedia Information Systems
Computer Communication Networks
Data Structures, Cryptology and Information Theory
Special Purpose and Application-Based Systems - 出版者：Springer Netherlands
- ISSN：1573-7721
文摘
Recent Large-Scale Multimedia Retrieval (LSMR) methods seem to heavily rely on analysing a large amount of data using high-performance machines. This paper aims to warn this research trend. We advocate that the above methods are useful only for recognising certain primitive meanings, knowledge about human interpretation is necessary to derive high-level meanings from primitive ones. We emphasise this by conducting a retrospective survey on machine-based methods which build classifiers based on features, and human-based methods which exploit user annotation and interaction. Our survey reveals that due to prioritising the generality and scalability for large-scale data, knowledge about human interpretation is left out by recent methods, while it was fully used in classical methods. Thus, we defend the importance of human-machine cooperation which incorporates the above knowledge into LSMR. In particular, we define its three future directions (cognition-based, ontology-based and adaptive learning) depending on types of knowledge, and suggest to explore each direction by considering its relation to the others.