Benchmarking of image features for content-based retrieval

Wei Ying Ma; Hong Jiang Zhang

doi:10.1109/acssc.1998.750865

Benchmarking of image features for content-based retrieval

Wei Ying Ma
, Hong Jiang Zhang

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

89 Citations (Scopus)

Abstract

A very fundamental issue in designing a content-based image retrieval system is to select the image features that best represent the image contents in a database. Such a selection requires a comprehensive evaluation of retrieval performance of image features. In this paper, we provide a detailed comparison of a number of commonly used color and texture features based on a large and diverse collection of image data. The investigated color features include color histogram, color moments, color coherence vectors and color correlogram with respect to different color spaces and quantizations. As for texture features, we compare Tamura features, edge histogram, MRSAR, Gabor texture feature, and wavelet transform features. The result of this experiment can be used as a benchmark for selecting features in a content-based image retrieval system.
© 1998 IEEE

Original language	English
Pages (from-to)	253-256
Journal	Conference Record of the Asilomar Conference on Signals, Systems and Computers
Volume	1
DOIs	https://doi.org/10.1109/acssc.1998.750865
Publication status	Published - 1998
Externally published	Yes
Event	Proceedings of the 1998 32nd Asilomar Conference on Signals, Systems & Computers. Part 1 (of 2) - Pacific Grove, CA, USA Duration: 1 Nov 1998 → 4 Nov 1998

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Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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abstract = "A very fundamental issue in designing a content-based image retrieval system is to select the image features that best represent the image contents in a database. Such a selection requires a comprehensive evaluation of retrieval performance of image features. In this paper, we provide a detailed comparison of a number of commonly used color and texture features based on a large and diverse collection of image data. The investigated color features include color histogram, color moments, color coherence vectors and color correlogram with respect to different color spaces and quantizations. As for texture features, we compare Tamura features, edge histogram, MRSAR, Gabor texture feature, and wavelet transform features. The result of this experiment can be used as a benchmark for selecting features in a content-based image retrieval system. {\textcopyright} 1998 IEEE",

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AU - Zhang, Hong Jiang

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AB - A very fundamental issue in designing a content-based image retrieval system is to select the image features that best represent the image contents in a database. Such a selection requires a comprehensive evaluation of retrieval performance of image features. In this paper, we provide a detailed comparison of a number of commonly used color and texture features based on a large and diverse collection of image data. The investigated color features include color histogram, color moments, color coherence vectors and color correlogram with respect to different color spaces and quantizations. As for texture features, we compare Tamura features, edge histogram, MRSAR, Gabor texture feature, and wavelet transform features. The result of this experiment can be used as a benchmark for selecting features in a content-based image retrieval system. © 1998 IEEE

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Y2 - 1 November 1998 through 4 November 1998

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Benchmarking of image features for content-based retrieval

Abstract

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