Comparison between K-Mean and C-Mean Clustering for CBIR Ritu Shrivastava, Khushbu Upadhyay, Raman Bhati Acropolis Institute of Technology and Research, Indore, India r_acro@rediffmail.com , bhati.raman@gmail.com , Durgesh Kumar Mishra, Acropolis Institute of Technology and Research, Indore, India mishra_research@rediffmail.com Abstract: Traditionally image is retrieved with the help of the associated tag which is added to the image while storing it in the database. This text based image retrieval is time consuming, laborious and expensive. In order to overcome these flaws content based image retrieval is proposed which avoid the use of textual description and retrieve the image based on their visual similarity. To achieve this images are clustered using clustering techniques. Clustering groups similar images based on some properties for efficient and faster retrieval. This paper compares two clustering techniques: K- mean and C-mean clustering used for Content Based Image Retrieval System. Keywords: CBIR, clusters, seed points, k- mean, C- mean I. INTRODUCTION An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords, or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotations. [3] Additionally, the increase in social web applications and the semantic web have inspired the development of several web-based image annotation tools. Image search is a specialized data search used to find images. To search for images, a user may provide query terms such as keyword, image file/link, or click on some image, and the system will return images "similar" to the query. [1] The similarity used for search criteria could be meta tags, color distribution in images, region/shape attributes, etc. • Text based image retrieval - search of images based on associated metadata such as keywords, text, etc. • Content based image retrieval (CBIR) – the application of computer vision to the image retrieval. CBIR aims at avoiding the use of textual descriptions and instead retrieves images based on their visual similarity to a user-supplied query image or user-specified image features. [3] Recently, content based image retrieval has gained much popularity and lots of research is going on to make the image retrieval easier and faster that to with minimum implementation cost. [4] II. LITERATURE SURVEY P. Sankara Rao.et. al. [1], proposed a neural network for content based image retrieval. The author first performs the clustering of the images available in the database using hierarchical and k- mean clustering. This clusters obtained is then supplied to the neural network which uses radial basis function to derive the relevant images supplied through user query. Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, and Angela Y. Wu [2], proposed an enhanced Lloyd’s algorithm which is simple to implement and compute and gives better results as compared to other k- mean heuristics available which are NP hard. Websites www.wikipedia.org [3], www.cs.cmu.edu [6], intranet.cs.man.ac.uk [7], provides general information and about the topic and gives the updates of the research work done till know. It also provides source code of these algorithms for different application in matlab. Y. Rui, T.S.Huang and S.-F.Chang [4], dicusses the technique used for image retrieval in past and challenges faced with these techniques. Also discuss the current progress in this field. Chang Wen Chen, Jiebo Luo and Kevin J. Parker [9], discusses the problem faced while using K- mean algorithm and propose adaptive k- mean algorithm, its working and advantage over simple K- mean. Weiling Cai, Songcan Chen, Daoqiang Zhang [10], discusses the fuzzy C- mean clustering its working and drawbacks. The paper propose that incorporating the loacal information in the objective function while clustering improve the performance of the algorithm and make it resistant to noise and outliers. III. COMPARATIVE ANALYSIS Clustering is another term for grouping; it’s an unsupervised form of learning. In clustering we assign some label to data points that are close to each Second International Conference on Computational Intelligence, Modelling and Simulation 978-0-7695-4262-1/10 $26.00 © 2010 IEEE DOI 10.1109/CIMSiM.2010.66 104 Second International Conference on Computational Intelligence, Modelling and Simulation 978-0-7695-4262-1/10 $26.00 © 2010 IEEE DOI 10.1109/CIMSiM.2010.66 117 Second International Conference on Computational Intelligence, Modelling and Simulation 978-0-7695-4262-1/10 $26.00 © 2010 IEEE DOI 10.1109/CIMSiM.2010.66 117