Study Of Image Representation And Similarity Measure By Unification Partially Ordered Structural Of Macro Features And Micro Features

In the process of pattern recognition, whether the feature representation is proper ornot, is the precondition to decide whether the subsequent classification results are high orlow; and also the important condition for the performance of the entire pattern recognitionsystem to be good or not. At present, the field of pattern recognition has twobranches---structural pattern recognition and statistical pattern recognition. How tocombine the statistical pattern recognition with the structural pattern recognition, learnfrom the other’ strong points to offset its weaknesses, and jointly fulfill the tasks of patternrecognition, are the new directions to solve the problems of pattern recognition.Towards the image feature representation and classification problem in the patternrecognition, and taking the combination of macro-structural features and micro-statisticalfeatures as the basic idea, partially ordered structure as the way of representation, anddistance measure as the method of classification, the paper builds the theoreticalframework and provides the similarity measure method for the unification representationof the partially ordered structure of the macroscopic features and microscopic features ofimages.Firstly, towards the primitive extraction and primitive relationship construction instructural pattern recognition, the ordered image structural feature expression models areput forward. Through the formal context where the image primitives are built, themulti-vectors in geometric algebra are applied to mark the positions of the imageprimitives, and then the relational graph is formulated according to the partial order of theattributes of the image primitives, thus obtaining the image structural feature expressionmeans based on the geometric algebra representation.Secondly, towards the representation of the image spatial feature description andsimilarity measure, the image feature extraction and classification methods by integratingthe image macro-structural features and micro-statistical features have been put forward.On one hand, the spatial structural features of the images are represented by the quadtreedecomposition, and then the labeled quadtree distance calculation method based on the geometric algebraic representation is proposed; on the other hand, on the basis of theimage quadtree decomposition, the regional covariance descriptor is applied to present akind of image multi-scale local feature extraction method, and then the covariance matrixdistance is used to measure the similarities of the microscopic features of images. Finally,the weighting fusion of the image macro-structural distance and micro-statistical featuresdistance is applied in the overall similarity measure of images. The paper conductsexperiments on the four groups of face image databases, and then the comparisons aremade with the methods in the current literature. The results of the experiments suggest thatthe method of integrating the micro-statistical characteristics and the macro-structuralcharacteristic for the representation and classification of the image features, can obtainhigher precision of classification. It is an effective pattern recognition method.Finally, the image multi-scale feature description representation and the similaritymeasure are researched. On one hand, the methods to obtain primitives in the structuralpattern recognition are extended to multi-scale field. The method is to apply theQuaternion Wavelet Transformation to decompose the images in a multi-scale manner, andthen regard the images in different scales as the image primitives; besides, the formalconcept analysis is applied to analyze and explore the ordered structures of the imageprimitives, and establish the attribute tree structures of the image primitives. On the otherhand, towards the node feature extraction issue of the attribute tree, the covariance matrixand singular value decomposition are taken as the tools to build the microstructurerepresentation of the image primitives in different scales, and the weighted relationshipbetween the parent-child nodes of the attribute tree is established, and finally constitutingthe image macroscopic and microscopic integrated multi-scale weighted attribute treerecognition pattern, and transform the image classification problem into the correspondingsimilarity measure issue of the attribute tree. On this basis, a kind of attribute tree distancecalculation method is designed. At last, some tests are conducted on the internationalstandard texture image gallery UIUC and a group of medical micro-CT dataset, and thenthe comparison is made with the traditional methods. The results of the experiment showthat the methods put forward can significantly improve the precision of classification.The results of the research show that the unification representation and similarity by the partially ordered structures of the macroscopic and microscopic features of image datamethod put forward by this paper，which has merits of unification of feature representation,making classification simple and facilitating the utilizing and generating of expertknowledge. This method is expected to obtain further development and improvement, andbe applied to other domains’ pattern recognition problems.