期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 37, 期 -, 页码 361-367出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2014.10.001
关键词
Dates classification; Local binary pattern; Weber local descriptor; Support vector machine
类别
资金
- Research Center, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia [RC140218]
In this paper, we proposed a system of automatically classifying different types of dates from their images. Different dates have various distinguished features that can be useful to recognize a particular date. These features include color, texture, and shape. In the proposed system, a color image of a date is decomposed into its color components. Then, local texture descriptor in the form of local binary pattern (LBP) or Weber local descriptor (WLD) histogram is applied to each of the components to encode the texture pattern of the date. The texture patterns from all the components are fused to describe the image. Fisher discrimination ratio (FDR) based feature selection is utilized to reduce the dimensionality of the feature set. Size and shape features are appended to the texture descriptors to fully describe the date. As a classifier, we use support vector machines. The proposed system achieves more than 98% accuracy to classify the dates. (C) 2014 Elsevier Ltd. All rights reserved.
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