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Image Classification and retrieval of image from a large database has a great relevance in the present Scenario. A lot of work for an efficient method of image retrieval from large database has been made in the recent surveys. Here we propose a mathematical model based on CBIR system that uses the deep neural architecture for classification where the inputs are fuzzy grassland image features. Grassland image features varies according to the varieties of grassland images available through satellite images and hence its classification is a complex process. This paper proposes a new method for classification in which the inputs to the Neural Network are fuzzified and transformed in such a way that it clusters around a pivot vector there by making the classification task less complicated. This classification procedure is established theoretically by developing a mathematical model based on Neural Network approximation with fuzzy inputs. This model brings a transformation from the input image feature space to the output approximation space through the composition of mapping between the hidden transformation spaces that helps to strengthen the function approximation to the desired output. The Graphical representation on Fig(i) throws an insight into the mathematical theory of a CBIR system which unifies the advantages of deep neural architecture and fuzzy approximators. The mathematical concepts such as open balls, metric, limits, continuity etc are incorporated to establish the necessary and sufficient condition in the fuzzy based neural system for better and clear image retrieval.

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