SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries

SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries (2001)

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by James Z. Wang , Jia Li , Gio Wiederhold

Venue:	IEEE Transactions on Pattern Analysis and Machine Intelligence
Citations:	307 - 28 self

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Datum	Value	Source
TITLE	SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries	user correction - Legacy Corrections
AUTHOR NAME	James Z. Wang	user correction - Legacy Corrections
AUTHOR NAME	Jia Li	user correction - Legacy Corrections
AUTHOR NAME	Gio Wiederhold	user correction - Legacy Corrections
ABSTRACT	The need for efficient content-based image retrieval has increased tremendously in many application areas such as biomedicine, military, commerce, education, and Web image classification and searching. We present here SIMPLIcity (Semanticssensitive Integrated Matching for Picture LIbraries), an image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction, and integrated region matching based upon image segmentation. As in other regionbased retrieval systems, an image is represented by a set of regions, roughly corresponding to objects, which are characterized by color, texture, shape, and location. The system classifies images into semantic categories, such as textured-nontextured, graphphotograph. Potentially, the categorization enhances retrieval by permitting semantically-adaptive searching methods and narrowing down the searching range in a database. A measure for the overall similarity between images is developed using a region-matching scheme that integrates properties of all the regions in the images. Compared with retrieval based on individual regions, the overall similarity approach 1) reduces the adverse effect of inaccurate segmentation, 2) helps to clarify the semantics of a particular region, and 3) enables a simple querying interface for region-based image retrieval systems. The application of SIMPLIcity to several databases, including a database of about 200,000 general-purpose images, has demonstrated that our system performs significantly better and faster than existing ones. The system is fairly robust to image alterations.	user correction - Legacy Corrections
YEAR	2001	INFERENCE
VENUE	IEEE Transactions on Pattern Analysis and Machine Intelligence	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	947--963	INFERENCE
VOLUME	23	INFERENCE
CITATIONS	30 found	ParsCit 1.0