CoIRS: cluster-oriented image retrieval system
CoIRS: cluster-oriented image retrieval system
复制标题
CoIRS：面向集群的图像检索系统
DOI：
10.1109/ictai.2004.39
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Adel Said Elmaghraby
中科院分区：
文献类型：
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作者：
Hewayda M. Lotfy;Adel Said Elmaghraby
基于区域的图像检索系统面临的一个主要问题是如何对区域进行适当的描述，以实现高效和有意义的检索。提出了一种新的面向聚类的图像检索系统。CoIRS的一个显着方面是它集成了鲁棒的无监督学习来检测区域。分割是基于局部颜色和纹理特征，允许基于集群或区域的搜索。此外，一个特权组件是构建包含簇质心、颜色、纹理和形状特征的簇签名（CS）。该系统还构建了区域特征（RS），其中包括基于区域的特征，如不变矩、面积和偏心率。此外，该系统的另一个显著特征是特征排名。三个特征被用来对签名进行排序，颜色、纹理或形状。CoIRS框架在精度估计的支持下提供了成功的检索结果。使用由不同类别图像组成的2000幅图像数据库对系统进行评估。
A major problem raised by a region-based image retrieval system is the proper description of regions for efficient and semantically meaningful retrieval. We present CoIRS a novel cluster oriented image retrieval system. A distinguishing aspect of CoIRS is its integration of a robust unsupervised learning for the detection of regions. The segmentation is based on local color and texture features that allows cluster- or region-based search. In addition, a privileged component is the constructing of cluster signatures (CS) that include, color, texture, and shape features of the clusters centroids. The system constructs region signatures (RS) as well which includes region based features such as the invariant moments, area, and eccentricity. Also, another distinctive feature of the system is Feature ranking. Three features were used for ranking the signatures, color, texture, or shape. CoIRS framework proved to provide successful retrieval results supported by precision estimation. The system is evaluated using a database of 2000 images composed of different categories of images.