Image acquisitions are increasing day by day due to progress in social networking and digital technologies. Nowadays, with the evolution of various image capturing devices, an enormous quantity of complex images is being produced.content-primarily based image Retrieval (CBIR) is the answer to access images without difficulty wherein proper indexing and association are required. It makes CBIR a distinguished field in computer vision research. There are several uses of CBIR systems in day to day life for example medical, internet, scientific research and various other communication media. In the CBIR system, the user gives a query to obtain images from large datasets having a large number of images. In information transfer via electronic media using particular formats of data, images play an essential role. The extraction of information from communicated images is necessary with extra processing. In this paper, a comparative survey has been carried out on different content-based image retrieval implementation. These methods are determined by several authors for the feature extraction process of images and classification. This will help to plan the strategy for optimizing the CBIR system.
We design a new language DR ω π F in which we extend the language DR ω π to handle node failure during communication. We have extended the reduction semantics of DR ω π. For handling the node failure we describe two tables at router: One is routing table contains name mapping for forward travel of messages and other is a database of all active nodes at any router. Further we discuss the new node creation and failed node activation during communication. We show how the entries of routing tables are updated using backward learning updates. We explain the usefulness and working of our reduction semantics by taking one example.
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