Knowledge Vision Engines: a New Generation of Image Understanding Systems, Combining Computational Intelligence Methods and Model-based Knowledge Representation and Reasoning

摘要

Vision is a part of a larger informational system that converts visual information into knowledge structures. These structures drive vision process, resolving ambiguity and uncertainty via feedback, and provide image understanding, that is an interpretation of visual information in terms of such knowledge models. The solution to Image Understanding problems is suggested in form of active multilevel hierarchical networks represented dually as discrete and continuous structures. Computational intelligence methods transform images into model-based knowledge representation. Certainty Dimension converts attractors in neural networks into fuzzy sets, preserving input-output relationships. Symbols naturally emerge in such networks. Symbolic Space is a dual structure that combines closed distributed space split by the set of fuzzy regions, and discrete set of symbols equivalent to the cores of regions represented as points in the Certainty dimension. Model Space carries knowledge in form of links and relations between the symbols, and supports graph, diagrammatic and topological operations. Composition of spaces works similar to M. Minsky frames and agents, Gerard Edelman's "maps of maps", etc., combining machine learning, classification and analogy together with induction, deduction and other methods of higher level model-based reasoning. Based on such principles, an Image Understanding system can convert images into knowledge models, effectively resolving uncertainty and ambiguity via feedback projections and does not require supercomputers.