Picture-graphics color image classification

Author

Prabhakar, Salil ; Cheng, Hui ; Handley, John C. ; Fan, Zhigang ; Lin, Ying-wei

Author_Institution

DigitalPersona Inc., Redwood City, CA, USA

Volume

2

fYear

2002

fDate

2002

Abstract

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.

Keywords

business graphics; colour graphics; edge detection; feature extraction; image classification; image colour analysis; image segmentation; image texture; business graphics; color images; edge characteristics; high-level classification; image classification; image processing; low-level image features; natural picture; neural network classifiers; picture-graphics; texture; tree classifiers; Classification algorithms; Color; Graphics; Image analysis; Image classification; Image databases; Image processing; Neural networks; Spatial databases; Tree graphs;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing. 2002. Proceedings. 2002 International Conference on

ISSN

1522-4880

Print_ISBN

0-7803-7622-6

Type

conf

DOI

10.1109/ICIP.2002.1040068

Filename

1040068