Research On Image Preprocessing Techniques In Image 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:Framework: We need a feasible framework to work over the image mining technology and the others.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.Feature description: In order to use the traditional data mining methods from rational data and databases in image mining area to discover and extract the precise knowledge, we should extract and describe the image basic and content features. We should pay more attention to developing new image features description methods.The curse of the dimensionality: After the image features extraction, we are amazed to find that too many feature dimensions need to be analyzed. If we want to mine the images efficiently and effectively using traditional data mining methods, we have to optimize or reduce the image feature dimensionality.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:Image Mining Framework: We put forward the feasible image mining processing framework after analyzing the function-driven image mining framework and information-driven image mining framework.Image Feature Extraction: The image mining is based on the image basic and semantic features, so it is the critical phase to represent and describe them. We pay more attention to the feature extraction and feature description from the image object. We propose the evaluation measures of the Zernike moment descriptor based on image reconstruction.The optimization of the image shape moment feature: There are too many moment values in the image shape moment vector if we want to describe the image shape feature precisely, which will increase the complexity of the moment computation and lead to the curse of the dimensionality of the image shape moment vector. We put forward the optimization algorithm of the image shape moment feature based on the evaluation computation to represent and describe image object shape feature with corresponding low moment order.Dimension reduction: The image feature data are usually high-dimensional through the image preprocessing phase from the image database or image sets. The common dimension reduction techniques include primary components analysis, factors analysis and so on, but the handling objects with these techniques are usually the numerical features. The new dimension reduction technique of the image high-dimensional feature data based on rough set theory is put forward in the dissertation.