Statistical multiscale blob features for classifying and retrieving image texture from large-scale databases

Statistical multiscale blob features for classifying and retrieving image texture from large-scale databases
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用于从大型数据库中分类和检索图像纹理的统计多尺度 blob 特征

DOI:
10.1117/1.3491420
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发表时间:
2010-10
影响因子:
1.1
通讯作者:
Wu, Haishan
Wu, Haishan
中科院分区:
计算机科学4区
文献类型:
--
作者:
Chen, Yan Qiu;Xu, Qi;Wu, Haishan

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从图像中提取纹理特征面临着两个新的挑战:包含多种纹理的大规模数据库,以及不同的成像条件。我们提出了一种称为多尺度斑点特征(MBF)的新方法来克服这两个困难。MBF分析分辨率比例和灰度级的纹理。提出的统计描述子有效地从分解后的二值图像中提取结构信息。实验结果表明,MBF在组合的大规模数据库(Vistex+Brodatz+Curet+OuTex)上的性能优于其他方法。此外,在伊利诺伊大学香槟分校数据库和整个Brodatz地图集上的实验结果表明,MBF对灰度缩放和图像旋转是不变的,并且在相当大的空间尺度范围内是健壮的。
The extraction of texture features from images faces two new challenges: large-scale databases with diversified textures, and varying imaging conditions. We propose a novel method termed multiscale blob features (MBF) to overcome these two difficulties. MBF analyzes textures in both resolution scale and gray level. Proposed statistical descriptors effectively extract structural information from the decomposed binary images. Experimental results show that MBF outperforms other methods on combined large-scale databases (VisTex+Brodatz+CUReT+OuTex). Moreover, experimental results on the University of Illinois at Urbana-Champaign database and the entire Brodatz's atlas show that MBF is invariant to gray-level scaling and image rotation, and is robust across a substantial range of spatial scaling.
DOI: 10.1109/tpami.2005.151
发表时间: 2005-08-01
影响因子: 23.6
作者:
Lazebnik, S;Schmid, C;Ponce, J
通讯作者: Ponce, J
DOI: 10.1109/icassp.1992.226274
发表时间: 1992-03
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