使用CBIR的四个统计时刻的平均查询来提高性能

H. B. Kekre, Kavita Sonawane
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引用次数: 2

摘要

本文解释了使用平均特征向量与单个查询图像特征向量相比的有效性,该特征向量将被发射到使用基于图像R, G和B平面的均衡直方图划分的bin方法设计的CBIR中。将27维特征向量提取到分别保存R、G、B颜色的前4个中心化绝对矩统计信息的箱子中。本文使用三种不同的相似性度量来比较查询图像和数据库图像,即绝对距离、欧几里得距离和余弦相关距离。在包含20个不同类别的100幅BMP图像的2000幅图像数据库中进行了实验。使用PRCP、LSRR和Longest String三个参数来评估本文所采用的CBIR方法的性能。
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Performance improvement using average query fired to bins of four statistical moments for CBIR
This paper explains the effectiveness of average feature vector used as compared to a single query image feature vector to be fired to the CBIR designed using bins approach based on the partitioning of the equalized histograms of R, G and B planes of images. The feature vectors of dimension 27 are extracted into bins holding the statistical information of first 4 centralize absolute moments of R, G and B colors separately. Three different similarity measures are used in this paper for comparing the query image and database images namely Absolute distance, Euclidean distance and Cosine correlation distance. Experimentation of this approach is demonstrated for image database of 2000 BMP images containing 100 images from 20 different classes. Three parameters are used namely PRCP, LSRR and Longest String to evaluate the performance of the approaches used in this paper for CBIR.
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