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An Intelligent Mining Framework for Building Map Image Database using Self Organizing Maps

By Animesh Tripathy and Prashanta Kumar Patra

Abstract

Abstract — Emergence of vast amounts of geographically calibrated image data is an estimation of geographic information of an image. Extracting information from images has always been considered to be complex as it depends upon the type and nature of the image and data existing in it. In this paper we apply traditional Colour Filter Array (CFA) approach to capture image then interpolate it to full RGB plane else can be kept in its twodimensional form taking less space. The analysis is based on aerial images using a purely data driven feature extraction approach. Image features are selected based on the position of the image objects using feature selection and secondary information. A neural network model is further used to self organize the aerial image maps. Intelligent Mining rules are then applied to image datasets using association rule to build an image mining database. Thus a framework is designed for reducing the storage space in the database which may then lead to faster execution of query

Topics: Index Terms—Aerial Image, Map Database, Correlation, Colour Thresholding, Colour Filter Array (CFA
Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.371.9979
Provided by: CiteSeerX
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