Research And Application Of Image Data Mining

With the development and maturity of the image acquisition and storage technology, especially the sharp increasing images on the Internet。We are compelled to face the huge great deal of image data and have no time to look at the image data and content in detail。People need the techniques and tools to analyze the image data, expecting to discover the underlying and useful knowledge and patterns in the image。Referring to the definition of the data mining by Fayyad, we can define image mining as the nontrivial process to discover valid, novel, potentially useful, and ultimately understandable knowledge from large image sets or image databases。In fact, from the view point of image understanding, we can combine the image mining and image understanding to research on the emerging image mining technology。There are many challenges in the image mining area and they are listed as follows:1. Framework:We need a feasible framework to work over the image mining technology and the others。2. Image preprocessing:The objects to be mined include not only the image data, but also the text data correlated with the image data in the image mining area。It is not a good method to use rational model to represent image data directly。In order to implement the image mining process, the image data must be effective preprocessed at first。The goals of the dissertation are to develop and use techniques and methods implementing the image mining task。The major contents and contributions are listed as follows:1. In this case, the paper brings forward a new framework model which is much simpler and more applied than before。The characteristic of this model is all the former approaches, models or the episteme can be used in the course of remounting of image mining, besides, users can learn or renew the domain knowledge by image sample training and alternating learning。2. This paper proposes an enhanced image classifier to extract patterns from images containing text using a combination of features。Image containing text can be divided into the following types:scene text image, caption text image and document image。A total of eight features including intensity histogram features and GLCM texture features are used to classify the images。In the first level of classification, the histogram features are extracted from grayscale images to separate document image from the others。In the second stage, the GLCM features are extracted from binary images to classify scene text and caption text images。In both stages, the decision tree classifier(DTC)is used for the classification。Experimental results have been obtained for a dataset of about 60 images of different types。This technique of classification has not been attempted before and its applications include preprocessing for indexing of images, for simplifying and speeding up Content Based Image Retrieval (CBIR) techniques and in areas of Machine Vision。...