A mathematical model of neuro-fuzzy approximation in image classification

Image digitization and explosion of World Wide Web has made traditional search for image, an inefficient method for retrieval of required grassland image data from large database. For a given input query image Content-Based Image Retrieval (CBIR) system retrieves the similar images from a large data...

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Bibliographic Details
Main Authors: Gopalan, Sasi, Pinto, Linu, C., Sheela, N., Arun Kumar M.
Format: Conference Proceeding
Language:English
Subjects:
Approximation
Art galleries & museums
Artificial neural networks
Back propagation networks
Digitization
Fuzzy logic
Fuzzy systems
Grasslands
Image classification
Image management
Image retrieval
Mathematical analysis
Mathematical models
Neural networks
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Summary:Image digitization and explosion of World Wide Web has made traditional search for image, an inefficient method for retrieval of required grassland image data from large database. For a given input query image Content-Based Image Retrieval (CBIR) system retrieves the similar images from a large database. Advances in technology has increased the use of grassland image data in diverse areas such has agriculture, art galleries, education, industry etc. In all the above mentioned diverse areas it is necessary to retrieve grassland image data efficiently from a large database to perform an assigned task and to make a suitable decision. A CBIR system based on grassland image properties and it uses the aid of a feed-forward back propagation neural network for an effective image retrieval is proposed in this paper. Fuzzy Memberships plays an important role in the input space of the proposed system which leads to a combined neural fuzzy approximation in image classification. The CBIR system with mathematical model in the proposed work gives more clarity about fuzzy-neuro approximation and the convergence of the image features in a grassland image.
ISSN:0094-243X
1551-7616
DOI:10.1063/1.4952304