Fusion of color histogram and LBP-based features for texture image retrieval and classification
Fusion of color histogram and LBP-based features for texture image retrieval and classification
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DOI：
10.1016/j.ins.2017.01.025
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发表时间：
2017-06-01
影响因子：
8.1
通讯作者：
Prasetyo, Heri
中科院分区：
文献类型：
--
作者：
Liu, Peizhong;Guo, Jing-Ming;Prasetyo, Heri
局部二值模式（LBP）算子及其变体作为图像特征提取算子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来提取图像的纹理信息。可以从LBP代码中导出单个或联合直方图，其可以在某些应用中用作图像特征描述符。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者，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。本文通过在图像检索和分类系统中加入一个附加的颜色特征，即颜色信息特征（CIF），沿着基于LBP的特征，克服了这个问题。基于CIF和LBP的特征充分体现了图像的颜色和纹理特征。如实验结果所示，混合CIF和LBP为基础的功能提出了一个有前途的结果，并优于现有的方法在几个图像数据库。因此，它可以是一个非常有竞争力的候选人在检索和分类应用。(C)2017爱思唯尔公司All rights reserved.
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. A single or join histogram can be derived from the LBP code which can be used as an image feature descriptor in some applications. 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 Information Feature (CIF), along with the LBP-based feature in the image retrieval and classification systems. The CIF and LBP-based feature adequately represent the color and texture features. As documented in the experimental result, the hybrid CIF and LBP-based feature presents a promising result and outperforms the existing methods over several image databases. Thus, it can be a very competitive candidate in retrieval and classification application. (C) 2017 Elsevier Inc. All rights reserved.