Perception based Framework for Full and Partial Blind Image interpretation using Neural Network
DOI:
https://doi.org/10.26438/ijcse/v6i5.11771182Keywords:
Artificial neural network (ANN, Full-Reference (FR), Human visual system (HVS, No-Reference (NR),, Reduced-Reference (RR), Receiver operating characteristics (ROC),, Self organizing feature map (SOFM)Abstract
With expeditious escalation of digital imaging and communication technologies, inspection of image is a utmost concern. One of the approaches to resolve this issue is to use neural networks. Neural network is fast and powerful scheme with a great ability to deal with noisy or incomplete information. HVS based framework proposed here aims to analyse two kinds of images: First one is fully blind image where there is no reference image available and second is partially blind image where reference image information is partially available as set of certain features. In the first case competitive leaning based self organizing feature map is used to train the network which makes clusters of the input no-reference images. Partially blind image analysis is achieved by training the network using unsupervised feature learning which classifies input in to specific classes on the basis of perceptual features. Many of the Existing methods utilize natural image statistics or probability distribution model which fails to differentiate images in accordance with subjective opinions. This paper considers perceived image features in order to properly classify different images which in turn ease image analysis.
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