Image retrieval based on enhanced micro-structure and context-sensitive similarity

doi:10.11772/j.issn.1001-9081.2014.10.2938

Abstract

Abstract:

A new image retrieval method based on enhanced micro-structure and context-sensitive similarity was proposed to overcome the shortcoming of high dimension of combined image feature and intangible combined weights. A new local pattern map was firstly used to create filter map, and then enhanced micro-structure descriptor was extracted based on color co-occurrence relationship. The descriptor combined several features with the same dimension as single color feature. Based on the extracted descriptor, normal distance between image pairs was calculated and sorted. Combined with the iterative context-sensitive similarity, the initial sorted image series were re-ranked. With setting the value of iteration times as 50 and considering the top 24 images in the retrieved image set, the comparative experiments with Multi-Texton Histogram (MTH) and Micro-Structure Descriptor (MSD) show that the retrieval precisions of the proposed algorithm respectively are increased by 13.14% and 7.09% on Corel-5000 image set and increased by 11.03% and 6.8% on Corel-10000 image set. By combining several features and using context information while keeping dimension unchanged, the new method can enhance the precision effectively.

摘要：

针对图像检索中多特征综合描述子维度过高且综合权值难以确定的缺点，提出一种新的基于增强微结构和上下文相似度的图像检索方法。首先，使用一种新的局部模式映射来创建过滤图；然后，基于该图上的颜色共生关系提取增强微结构描述子，该描述子综合了多种特征而维度与单特征相同，检索时使用此描述子计算图像对间的规范距离得出初始的有序相似图像序列；最后，结合迭代上下文相似度对检索序列进行重新排序。当迭代次数为50且考虑前24幅结果图像时，在Corel-5000和Corel-10000图像集上的实验结果显示，所提方法与同类型的多重基元直方图(MTH)和微结构描述子(MSD)方法相比，检索查准率分别提高了13.14%、7.09%和11.03%、6.8%。结果表明新方法能在维度不变的情况下综合多种特征并充分利用上下文信息，从而有效提高图像检索的准确率。

CLC Number:

TP391.41

HU Yangbo YUAN Jie WANG Lidong. Image retrieval based on enhanced micro-structure and context-sensitive similarity[J]. Journal of Computer Applications, 2014, 34(10): 2938-2943.

胡扬波袁杰王李冬. 基于增强微结构和上下文相似度的图像检索[J]. 计算机应用, 2014, 34(10): 2938-2943.

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