Enhancement of Big Image Processing Using Naive Bayes-based Logistic Regression

Remani Naga Venkata Jagan Mohan

Abstract


Nowadays, the digital substance is generated with exponential pace due to the abundant use of scanners and video cameras. As the number of digital images is ever increasing, the aim of storing and processing the colossal database has become a hard task. According to the recent surveys, most of the digital data available today is unstructured such as video, audio, images or text. Classification is playing a very essential role for offline/online based image processing. Due to Large-scale geological image data, classification at the level of pixels is necessitated. In this paper, we proposed a classification approach on raster based spatial image data using Naïve Bayes-based Logistic Regression analysis. In this study, the images of natural resources in raster data form are used to get thematic maps. The main aim of this research is to check whether all the objects are conditionally independent of the given class, which can be used to get the more bias and lower variance. Finally, the experiments are conducted on spatial big images in order to increase the efficiency of the process for this system.

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MAYFEB Journal of Electrical and Computer Engineering
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