文章基本信息

标题：Web Image Retrieval Using Clustering Approaches
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作者：Umesh K K ; Suresha
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2011
卷号：1
期号：3
页码：27-36
DOI：10.5121/csit.2011.1304
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Image retrieval system is an active area to propose a new approach to retrieve images from the large image database. In this concerned, we proposed an algorithm to represent images using divisive based and partitioned based clustering approaches. The HSV color component and Haar wavelet transform is used to extract image features. These features are taken to segment an image to obtain objects. For segmenting an image, we used modified k-means clustering algorithm to group similar pixel together into K groups with cluster centers. To modify K-means, we proposed a divisive based clustering algorithm to determine the number of cluster and get back with number of cluster to k-means to obtain significant object groups. In addition, we also discussed the similarity distance measure using threshold value and object uniqueness to quantify the results.
关键词：Feature Extraction; Pixel Segmentation; Modified k-means Clustering; Similarity Distance ;Measure; Object Clustering; Object Uniqueness