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Statistical Mining on Image Database Using Supervised Classification

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Document pages: 5 pages

Abstract: We present a protocol for the evaluation of a content-based remote sensing image information mining system. The knowledge driven information mining system (KIM) which easily enables users the access to remote sensing image archives via internet communication, the analysis of information details by an intelligent man-machine interface (MMI) The query performance of content based image retrieval system mainly depends on the datasets stored in the Clustered Database. We analyze the complexity of image data. In order to provide users fast access to the content of Clustered Database, the system is composed of two main modules. The first includes computationally intensive algorithms for off-line data ingestion in the database image. The next module consists of a graphical man–machine interface that manages the information fusion for interactive interpretation and the image information mining functions. Bayesian classification determines the accuracy of interactive training. In this interactive system user effort, characteristics of the internet design, guidance provided by the system, and duration of a user session are critical aspects, which are observed and measured.

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