Presentation	2003/7/11 Semantic Approach to Image Database Classification and Retrieval Takumi OHASHI, Zaher AGHBARI, Akifumi MAKINOUCHI,
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Abstract(in English)	This paper demonstrates an approach to image retrieval founded on classifying image regions hierarchically based on their semantics (e.g. sky, snow, rocks, etc.) that resemble peoples' perception rather than on low-level features (e.g. color, texture, shape, etc.). Particularly, we consider outdoor images and automatically classify their regions based on their semantics using the support vector machines (SVMs) tool. First, image regions are segmented using the hill-climbing method. Then, those regions are classified by the SVMs. The SVMs learns the semantics of specified classes from a test database of image regions. Such semantic classification allows the implementation of intuitive query interface. As we show in our experiments, the high precision of semantic classification justifies the feasibility of our approach.
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Paper #	DE2003-96
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Committee	DE
Conference Date	2003/7/11(1days)
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Registration To	Data Engineering (DE)
Language	ENG
Title (in Japanese)	(See Japanese page)
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Title (in English)	Semantic Approach to Image Database Classification and Retrieval
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1st Author's Name	Takumi OHASHI
1st Author's Affiliation	()
2nd Author's Name	Zaher AGHBARI
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3rd Author's Name	Akifumi MAKINOUCHI
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Date	2003/7/11
Paper #	DE2003-96
Volume (vol)	vol.103
Number (no)	192
Page	pp.pp.-
#Pages	6
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