IMTA 2010 - Image Mining Theory and Applications

Automation of image mining is one of the most important strategic goals in image analysis, recognition and understanding science and technologies. The main subgoals are developing and applying of mathematical theory for constructing image models accepted by efficient pattern recognition algorithms and for standardized representation and selection of image analysis transforms. Taking as a strategic goal the automated image mining it is necessary to provide image analysis professionals and final users with the following opportunities: - automated design, test and adaptation of techniques and algorithms for image recognition, estimation and understanding; - automated selection of techniques and algorithms for image recognition, estimation and understanding; - automated testing of the raw data quality and suitability for solving the image recognition problem; - standard technological schemes for image recognition, estimation, understanding and retrieval. Automation of image-mining is possible by combining and applying of different mathematical techniques for image analysis, understanding and recognition, in particular, of algebraic and discrete mathematics techniques. Automation of image processing, analysis, estimating and understanding is one of the crucial points of theoretical computer science having decisive importance for applications, in particular, for diversification of solvable problem types and for increasing the efficiency of problem solving. The role of an image as an analysis and estimation object is determined by its specific and inalienable informational properties. Image is a mixture and a combination of initial (raw, "real") data and its representation means, of computational and physical nature and models of objects, events and processes to be represented via an image. The specificity, complexity and difficulties of image analysis and estimation (IAE) problems stem from necessity to achieve some balance between such highly contradictory factors as goals and tasks of a problem solving, the nature of visual perception, ways and means of an image acquisition, formation, reproduction and rendering, and state of the art in the mathematical, computational and technological means allowable for the IAE. We may consider that the main contradiction is related to the "pictorial nature" of an image and the " formal" (symbolic) foundations of IAE: it is well known that to take an advantage from data representation an image form is necessary to reduce the latter to a "non-image" form. In IAE is used a wide spectrum of mathematical techniques from algebra, geometry, discrete mathematics, mathematical logics, probability theory, mathematical statistics, calculus, as well as the techniques of mathematical theory of pattern recognition, digital signal processing, and physics (in particular, optics). The mathematical theory of image analysis is not finished and is passing through a developing stage. It is only recently came understanding of the fact that only intensive creating of comprehensive mathematical theory of image analysis and recognition (in addition to the mathematical theory of pattern recognition) could bring a real opportunity to solve efficiently application problems via extracting from images the information necessary for intellectual decision making. The transition to practical, reliable and efficient automation of image-mining is directly dependent on introducing and developing of mathematical means for IAE. The natural way to overcome the above mentioned contradiction between "pictorial nature" of an image and the " formal" (symbolic) foundations of IAE is to introduce pattern recognition oriented image models and necessary means and techniques for reduction of an image to a recognizable form without loss of image s