Research On Cultural Calculation Of Paper-cutting

Culture is the sum of material wealth and spiritual wealth createdduring the human development process in the social history. Itespecially refers to the spiritual wealth, such as literature, art,education and science, etc. Culture bears rules that can be detectedby computer methods. Transforming rule discovery and analysis ofcultural issues into quantitative calculation problem is a new researchfield. The value of cultural calculation is to can make full use ofexisting data and to dig out deep cultural rules. The earliest researchwork about culture calculation (Michel et al.,2010) regarded thewords in the literature as cultural genes. Researches designed a tool tospeculate the emergence and evolution of cultural trends according tothe time variation. The tool can be used to generate cultural timelinebased on a specific word frequently or a few used regular to showcultural peaks or troughs. Text retrieval technology has beendeveloped into a new era, but there are still some difficulties toretrieve non-text resources such as image, video and audio. In thispaper, we take paper-cut as a specific object to discuss the problemabout image cultural calculation. The following issues were analyzed in the paper.(1) According to the paper-cut patterns digitized representation,content retrieval and cultural calculation problems, an XML based datastorage model of paper-cut is put forward. The main structure of themodel and the conversion method from paper-cut patterns to XMLdocument are be discussed. The proposed model theory is also appliesto other image content retrieval. Based on the realization of two-waytransform from paper-cut to XML data file, we added the requiredinformation for content retrieval and cultural calculation in the model toenrich its function. Test results show that using this model we can storepaper-cut efficiently and can support the subsequent data mining andcultural calculation on paper-cut.(2)Designed a graph standardized method (PNF) for the graphsdescribed by the boundary corners. The paper discussed therelationships among similar planar particle graphs whose position,structure and type are same. PNF is invariant to translation, scaling androtation. Put forward the characteristic matrix method and cyclic vectorapproach to achieve the PNF algorithms. PNF is particularly suitable forthe semi-structured data storage, so that in the field of patternrecognition and semi-structured data mining PNF has many potentialapplications.(3) On the basis of the PNF research results we presented symbolic method for images, and designed similarity algorithms to calculatesymbolic spatial relationships between images. This method can identifythe positional relationship between symbolic images containingrepeated symbols. Experimental results showed that the algorithm waseffective.(4) Based on the research of PNF, emphatically analyzed its abilityto support symmetric detection. After analyzing the PNF standardizationprocess, modified the PNF rules for graphics symmetric detectionproblems. Presented the concept of positive and negative energy of PNFcoordinate sequences, and further improve the efficiency of symmetricdetection on the basis of the pretreatment.