期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2012
卷号：3
期号：3
页码：790-795
语种：English
出版社：Ayushmaan Technologies
摘要：Clustering is a form of unsupervised classification that aims at grouping data points based on similarity. In this paper, we propose a new partitional clustering algorithm based on the notion of ‘contribution of a data point’. We apply the algorithm to contentbased image retrieval and compare its performance with that of the k-means clustering algorithm. Unlike the k-means algorithm, our algorithm optimizes on both intra-cluster and inter-cluster similarity measures. It has three passes and each pass has the same time complexity as an iteration in the k-means algorithm. Content based image retrieval (CBIR) is done using the image feature set extracted from Haar Wavelets applied on the image at various levels of Decomposition. Here the database image features are extracted by applying Haar Wavelets on gray plane (average of red, green and blue) and color planes (red, green and blue components). Our experiments on a bench mark image data set reveal that our algorithm improves on the recall at the cost of precision.The results show that precision and recall of Haar Wavelets are better than complete Haar transform based CBIR, which proves that Haar Wavelets gives better discrimination capability in image retrieval at higher query execution speed, per higher level Haar Wavelets.
