Emphasis on rough Set theory for image retrieval

This paper highlights concept of rough Set theory and focus on its active participation in various technologies such as cloud computing, artificial intelligence, data mining, expert system etc. we focus particularly on exploitation of rough set theory image retrieval with hybridization approach. We start with fundamental of rough set theory, its recent development, application, trends, impact on recent technology like cloud computing. In simple term, RS theory work or analysis on the basis of information associated with objects or events. Rough set theory is promising theory that work on intelligent analysis of imperfect data and produce meaningful information related to data according to requirement of application. Other theories like Bayesian inference or Fuzzy set theory are complementary theory that work parallel with Rough set theory on rough data or imperfect data, which is important and basic processing step of many application like search engine, data mining, web mining etc. In image retrieval, rough set theory play important role in classification, clustering, feature selection, feature extraction, rule learning etc. Our interest is to study its various combinational hybridized methods used in image retrieval to address semantic gap. We are proposed content based image retrieval framework with rough set theory for addressing semantic gap by using semantic classifier with semantics decision rule based on image information system. This framework also improves precision, recall and accuracy of image retrieval.