Texture images retrieval based on Float-LBP

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (12): 3545-3548.

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Texture images retrieval based on Float-LBP

ZHAO Yudan¹,WANG Qian²,FAN Jiulun¹

1. School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an Shaanxi 710121, China;
2. National Laboratory of the Ministry of Public Security-Electronic Information Processing Techniques in Criminal Investigation Applications,(Xi'an University of Posts and Telecommunications), Xi'an Shaanxi 710121, China

Received:2014-07-01 Revised:2014-08-23 Online:2014-12-31 Published:2014-12-01
Contact: ZHAO Yudan
Supported by:
;Scientific Research Program Funded by Shaanxi

基于Float-LBP算法的纹理图像检索

赵玉丹¹,王倩²,范九伦¹

1. 西安邮电大学通信与信息工程学院,西安710121;
2. 图像与信息处理研究所及公安部重点实验室(西安邮电大学),西安 710121

通讯作者: 赵玉丹
作者简介:赵玉丹(1987-）,女,山东菏泽人,硕士研究生,主要研究方向:现代信号处理；王倩(1983-）,女,陕西西安人,讲师,博士,主要研究方向:模式识别、智能系统；范九伦(1964-）,男,河南温县人,教授,博士,主要研究方向:模糊集、模糊信息处理、图像处理。
基金资助:
国家自然科学基金资助项目;陕西省教育厅科研计划项目

Abstract

Abstract:

An improved method based on Local Binary Pattern (LBP) was proposed to solve the problem that the representing ability of LBP is bad because only the relationship between neighbors and the central pixels are considered while the floating relationship of the gray values in the neighbor region is ignored. Firstly, each neighbor was compared clockwise with its next adjacent neighbor before threshold and an LBP-like code was generated. Secondly, the code was encoded to a decimal number named as Float-LBP (F-LBP). Thirdly, the features extracted by the F-LBP and the basic LBP operators were combined together. The experimental results show that the combination of the F-LBP and the basic LBP operators can improve the retrieval accuracy by extracting more discriminative information while reserving the local micro-texture.

摘要：

局部二值模式(LBP）仅依赖中心点和其邻域点在灰度上的差异,忽略了邻域点之间的浮动关系,导致表征能力较差,因此提出了一种改进的LBP算法。在对图像LBP二值化前,首先将邻域点按顺时针方向和其相邻的点进行比较得到一串类似于LBP算子的二进制序列；然后对这一序列进行十进制编码,并将这一部分的特征记作Float-LBP(F-LBP)；最后再和原始的LBP算子提取的特征结合作为整幅图像的纹理特征。实验表明,通过F-LBP和LBP算子的结合,在保留局部微观纹理的前提下增加了更多的图像纹理分布信息,提高了算法的检索精度。

CLC Number:

TP391.4

ZHAO Yudan WANG Qian FAN Jiulun. Texture images retrieval based on Float-LBP[J]. Journal of Computer Applications, 2014, 34(12): 3545-3548.

赵玉丹王倩范九伦. 基于Float-LBP算法的纹理图像检索[J]. 计算机应用, 2014, 34(12): 3545-3548.

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Texture images retrieval based on Float-LBP

基于Float-LBP算法的纹理图像检索

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