Incorporating color feature on LBP-based image retrieval

Jing-Ming Guo, Heri Prasetyo
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Abstract

The Local Binary Pattern (LBP) operator and its variants play an important role as the image feature extractor in the textural image retrieval and classification. The LBP-based operator extracts the textural information of an image by considering the neighboring pixel values. However, the LBP-based feature is not a good candidate in capturing the color information of an image, making it is less suitable for measuring the similarity of color images with rich color information. This work overcomes this problem by adding an additional color feature, namely Color Histogram Feature (CHF), along with the LBP-based feature in the image retrieval domain. Experimental result shows that the hybrid CHF and LBP-based feature presents a promising result and outperforms the existing methods over several image databases.
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结合颜色特征的lbp图像检索
局部二值模式算子及其变体作为图像特征提取器在纹理图像检索和分类中起着重要的作用。基于lbp的算子通过考虑相邻像素值提取图像的纹理信息。然而,基于lbp的特征在捕获图像的颜色信息方面不是一个很好的候选,使得它不太适合测量具有丰富颜色信息的彩色图像的相似性。这项工作通过在图像检索域中添加一个额外的颜色特征,即颜色直方图特征(CHF),以及基于lbp的特征来克服这个问题。实验结果表明,基于CHF和lbp的混合特征在多种图像数据库上取得了很好的效果,并且优于现有的方法。
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