A Comprehensive Survey on Data Mining Techniques for Logical Analysis of Data in Content Based Image Retrieval System

This paper provides a comprehensive survey of the recent technical achievements in high level semantic based image retrieval. It identifies five major categories of the state of the art techniques in narrowing down the semantic gap. Those are (a) Data Mining techniques for the data analysis, data accessing and knowledge discovery processor to show experimentally and practically that how consistent, able and fast are these techniques for the study in the particular field, A solid mathematical threshold (0 to 1) is set to analyze the data. (b) Low level image features like Color, Texture, Shape and Spatial location [1]. (c) Similarity measurement and reducing the semantic gap. (d) A constructed decision tree presents effective models of decision-making, which can be learned to support image classification by the expert. A tool for data mining and image processing is presented and its application to image mining is shown on the task of Hep-2 cell-image classification. (e) Most reports of CBIR systems provide only qualitative measures of performance based on how similar retrieved images are to a target. Experiment 2 puts Picture Hunter into this context with a more rigorous test. We first establish a baseline for our database by measuring the time required to find an image that is similar to a target when the images are presented in random order. To implement a full-fledged image retrieval system with high-level semantics requires, the integration of salient low-level feature extraction, effective learning of high-level semantics, friendly user interface, and efficient indexing tool.