Compact Multi-dimensional LBP Features for Improved Texture Retrieval

N. Doshi, G. Schaefer
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引用次数: 1

Abstract

Content-based image retrieval has become an important research area and consequently well performing retrieval algorithms are highly sought after. Texture features are often crucial for retrieval applications to achieve high precision, while local binary pattern (LBP) based texture descriptors have been shown to work well in this context. LBP features decsribe the texture neighbourhood of a pixel using simple comparison operators, and are often calculated based on varying neighbourhood radii to provide multi-resolution texture description. Furthermore, local contrast information can be integrated into LBP leading to LBP variance (LBPV) features. In conventional LBP methods, the histograms corresponding to different radii are simply concatenated resulting in a loss of information between different resolutions and added ambiguity. In this paper, we show that multi-dimensional LBP and LBPV histograms, which preserve the relationships between scales, provide improved texture retrieval performance. To cope with the exponential increase in terms of feature length, we show that application of principal component based feature reduction leads to very compact texture descriptors with high retrieval accuracy.
改进纹理检索的紧凑多维LBP特征
基于内容的图像检索已成为一个重要的研究领域,性能良好的检索算法受到人们的高度追捧。纹理特征是实现高精度检索应用的关键,而基于局部二值模式(LBP)的纹理描述符在这方面表现良好。LBP特征使用简单的比较算子来描述像素的纹理邻域,并且通常基于不同的邻域半径来计算以提供多分辨率纹理描述。此外,局部对比信息可以整合到LBP中,从而得到LBP方差(LBPV)特征。在传统的LBP方法中,不同半径对应的直方图简单地连接在一起,导致不同分辨率之间的信息丢失,增加了模糊性。在本文中,我们证明了多维LBP和LBPV直方图,保留了尺度之间的关系,提供了改进的纹理检索性能。为了应对特征长度呈指数增长的情况,我们表明,应用基于主成分的特征约简可以得到非常紧凑的纹理描述子,并且检索精度很高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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