Semantic fuzzy Colour Algorithm for Image Retrieval Using Hindi Dialects

Abstract: Retrieval of an image by segmenting of an image into meaningful regions that can be read by machines and co- related with human language and the labeling of the regions as per concepts is a challenging problem. A framework for simultaneous image segmentation and object labeling leading which focuses on semantic analysis of images stored in the database has been done and it contributes to knowledge-assisted multimedia analysis and has been reducing the gap between semantics and low level visual features. The proposed framework operates at semantic level using possible semantic labels, represented by formal logic as fuzzy sets, to make decisions on handling image regions instead of visual features used traditionally. Contextual information associated with each image is based on a novel semantic processing methodology used , employing fuzzy scale algorithm and taxonomic knowledge representation. So there is a need here is to develop a technique of latent correlation between low level features and high level concepts and merging them in such a way that perspectives , feelings ,expressions about the colour are mapped as human’s can understand. It has been acknowledged that research has produced many algorithms for automatic image segmentation, as well as structuring of contents of the image based on English dialects, a context representation approach to use on top of semantic region growing. We introduce a methodology to improve the results of image retrieval, based on contextual information in hindi language dialects. A novel representation for context is introduced, combining fuzzy extraction and fuzzy scale with characteristics derived from the Semantic of concepts of colour.

Keywords: CBIR, Syntactical Feature, Feature Extraction, Multidimensional indexing, Fuzzy set, Fuzzy scale, linguistic labeling