Research And Construction Of Auto-Classification System For Renderings Of Environmental Art Based On Expression Techniques

The application of image classification technology in design industry enables digitization and management of design, and users can draw upon designs that conform to certain standards in a convenient, accurate and highly efficient way. The author hereby puts forward a method of automatic classification for renderings of environmental art based on expression techniques, with the aim of automatically constructing classification models by employing machine learning in order to provide designers with a conceptualized browsing method for reference.First, this paper offers an introduction to and analysis of common expression techniques that are utilized in environmental design, including black-white, colored pencil, gouache&watercolor, marker, computer expression, etc. The low-level features reflected by each kind of expression technique are listed under the perception of environmental art designers. Through the analysis of visual characteristics of various expression techniques, this paper extracts the features of various expression techniques based on the visual differences in color, texture, smoothness and degree of saturation, and select some algorithms suitable for art images'feature extraction from several ones.Second, this paper studies several important image classification methods and discusses and analyzes the possible options under different application backgrounds. Owing to the advantages of SVM such as firm theoretical basis and excellent classification function, this paper focuses on the research of SVM. The SVM is a method for solving two-class classification problems and cannot be applied to multi-class classification problems directly; hence this paper introduces the multi-class classification algorithm based on binary tree. Through the analysis of its principles and classification methods, and the visual features of expression of environmental design, constructs a BT-SVM classifier to learn, train and classify the renderings of environmental art. This paper conducts a study on three essential factors that affect the classification effect, including parameter optimization of kernel function, data normalization and number of samples. It summarizes the advantages and shortages of these factors in order to select the most appropriate model of SVM. In respect to the design of system interface, this paper puts forward the method of adopting WebBrowser control in MATLAB GUI to realize the display of image classification results, which not only can browse many images under the MATLAB GUI window, but also realizes the effect of clicking a small image and displaying a large image in MATLAB user interface by utilizing the good scalability of HTML language. Experiments have proved that the classification of environmental art images based on SVM suggested in this paper is workable, and other art works created by utilizing different expression techniques, which also has good classification effect.Finally, this paper looks into the future of the classification technology of images based on artistic style from selection of adaptive kernel function, gradual integration of relevant feedback mechanism and other aspects.