Classification of Textured and Non-textured Images
Using Region Segmentation

Jia Li, James Z. Wang, Gio Wiederhold
Stanford University, Stanford, CA 94305
Abstract:

The classification of general-purpose photographs into textured and non-textured images is critical for developing accurate content-based image retrieval systems for large-scale image databases. With the accurate detection of textured images, we may retrieve images based on features tailored for the corresponding image type. In this paper, we present an algorithm to classify a photographic image as textured or non-textured using region segmentation and statistical testing. The application of the system to a database of about $60,000$ general-purpose images shows much improved accuracy in retrieval.


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Citation: Jia Li, James Z. Wang and Gio Wiederhold, ``Classification of Textured and Non-Textured Images Using Region Segmentation,'' Proc. IEEE International Conference on Image Processing (ICIP), Vancouver, BC, Canada, pp. 754-757, IEEE, September 2000.

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Last Modified: July 10 2000
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