Review on Content Based Image Retrieval Systems Using Clustering Techniques

Over the last 10 years, the availability of large and steadily growing amounts of visual and multimedia data and the development of the Internet underline the need to create thematic access methods that offer more than simple text based queries or requests based on matching exact database fields. Content Based Image Retrieval (CBIR) has been one of the most vivid research areas in the field of computer vision that is, the problem of searching for digital images in large databases. Content based" means that the search will analyze the actual contents of the image. Content Based Image Retrieval (CBIR) aims at developing techniques that support effective searching and browsing of large image digital libraries based on automatically derived image features. In this Image retrieval system, query results are a set of images sorted by feature similarities with respect to the query. However images with high feature similarities to the query may be very different from the query in terms of semantics. Clustering is a form of unsupervised classification that aims at grouping data points based on similarity. In this paper, various clustering techniques are given. After reading all the technique we analyze that the complexity of computational terms is increasing subsequently.