A Novel Approach for Image Recognition to Enhance the Quality of Decision Making by Applying Degree of Correlation Using Artificial Neural Networks

Abstract

Many diversified applications do exist in science & technology, which make use of the primary theory of a recognition phenomenon as one of its solutions. Recognition scenario is incorporated with a set of decisions and the action according to the decision purely relies on the quality of extracted information on utmost applications. Thus, the quality decision making absolutely reckons on processing momentum and precision which are entirely coupled with recognition methodology. In this article, a latest rule is formulated based on the degree of correlation to characterize the generalized recognition constraint and the application is explored with respect to image based information extraction. Machine learning based perception called feed forward architecture of Artificial Neural Network has been applied to attain the expected eminence of elucidation. The proposed method furnishes extraordinary advantages such as less memory requirements, extremely high level security for storing data, exceptional speed and gentle implementation approach.

What came out of visual memory: Inferences from decay of difference-thresholds

“…This conclusion is now consistent with the above more recent evidence that recall of a stimulus is accompanied by cortical activity in similar locations or by similar patterns of activity as when those stimuli are viewed. Similar responses during sensory and memory processes are also consistent with models of recognition based on correlational operations (e.g., Austin & Lees, 2000;Dara, Satyanarayana, & Govardhan, 2014;Metcalfe, 1991;Mewhort & Johns, 2005;Pribram, Nuwer, & Baron, 1974). Similar sensory and memory processes are also consistent with evidence that consciousness and imagery are mediated by cortico-thalamic and cortico-cortical loops (e.g., Guo et al, 2017;Pergola, Ranft, Mathias, & Suchan, 2013).…”

“…This simulates the iterative matching procedure used by the observers in Experiments 1, 2, 3, and 5. Recognition by correlation has been widely used in memory models (e.g., Dara, Satyanarayana, & Govardhan, 2014;Metcalfe, 1991;Mewhort & Johns, 2005;Pribram et al, 1974). Neural correlates of similarity computations have been observed in the visual cortex (e.g., Mikhailova et al, 2017;van Vugt, Sekuler, Wilson, & Kahana, 2013).…”