Picture-graphics color image classification

High-level (semantic) image classification can be achieved by analysis of low-level image attributes geared for the particular classes. In this paper, we have proposed a novel application of the known image processing and classification techniques to achieve such a high-level classification of color...

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Bibliographic Details
Main Authors: Prabhakar, S., Hui Cheng, Handley, J.C., Zhigang Fan, Ying-wei Lin
Format: Conference Proceeding
Language:English
Subjects:
Classification algorithms
Color
Graphics
Image analysis
Image classification
Image databases
Image processing
Neural networks
Spatial databases
Tree graphs
Online Access:Request full text
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Summary:High-level (semantic) image classification can be achieved by analysis of low-level image attributes geared for the particular classes. In this paper, we have proposed a novel application of the known image processing and classification techniques to achieve such a high-level classification of color images. Our image classification algorithm uses three low-level image features: texture, color, and edge characteristics to classify a color image into two classes: business graphics or natural picture. We have achieved an accuracy of 96.6% on our database of 209 images using a combination of tree and neural network classifiers.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2002.1040068