Researches For Key Issues And Methods In Image Understanding

Image understanding is the hotspot and difficulty in computer reseach area. The essential task is to interpret the acquired image scene and its contents accurately. It is closely relative with computer vision and artificial intelligence with important theories and wide applications.Image understanding has the distinct layer property. As the visual information in lower layer, the theorical startpoint is computer vision and as the knowledge information in higher one, the theorical basis is artificial intelligence. Visual data and knowledge are two types of information through understanding images, but current researches on them is often separative which neglect the fusions between knowledge and data and ignore the relations between process in lower layer and analysis in higher one.Considering key issues about data-driven knowledge and knowledge-guiding data in image understanding, we start researches for novel methods from joints between these two kinds of information processing. The thesis focuses on representation, storage, analysis and transform with data and knowledge in image understanding to research proper cognitive carriers and knowledge processing methods for several sub-tasks as generic object detection and recognition, regional semantic understanding and scene analysis which forms the novel way. At the same time, we discuss the structures in image understanding and build models for objects with spatial relations and global scenes to represent corresponding restriction and feedback mechanisms, which guide for knowledge accuqusition and act on data processing in lower layer to improve speed and accuracy in image understanding and rm novel complete effective and rapid archetypal structure initially.This thesis includes the following contents:1,On the research of fusion with data and knowledge representation, we describe the general ways of information representation with emphasis on fusion and translation between knowledge and data to reveal cognitive relations in entities. Then we summarize the feature extraction strategies and build the regional statistical models with pixels. Based on them, a new object location method is proposed to keep out the "background" noise and supply the current ways for feature extraction.2,We study the storage and analysis on visual information to solve the graphic models as carriers in image understanding. We ummarize the theories for parameter estimation and probability inference with corresponding visual. Then we present an undirected graphic model based on spatial relations, discuss two main problems above and obtain the iterative equations to analyze object and scene for enrichment in image understanding.3,We discuss the cognitive division in visual information for generic object detection and recognition and propose the layer joint boosting algorithms based on sharing features. With the condition of approximate unchanged detection rate, the recognition rate increases and classification time decreases dramatically to show gradualness in image understanding and transform from visual data to knowledge.4,We research the knowledge processing and analysis in image understanding to solve the problems in regional analysis and semantics labeling. We present the new image segmentation and knowledge base reduction methods with rough set theories. The result demonstrates the better segmentation performance on visual consistent area and effect reduction without changes in conception classifications to avoid interference with noisy data and improve reasonability in labeling semantics and analyzing regions to some extend realizing the fusion with data and knowledge.5,We analyze the basic method for scene classification primarily and propose the new method based on Gaussian probabilistic statistical models for effect results. At the same time, we also validate the classification results as prior knowledge have strong guidance and restrict to improve accuracy in object analysis and reveal feedback in image understanding.