Method for sorting colors of colorful three-dimensional model based on neural network

Abstract

The invention provides a method for classifying color of a color three-dimensional model based on a neural network. A three-dimensional model aiming at an actual object is obtained through three-dimensional scanning equipment or virtual modeling software; a three-dimensional model database is built; the three-dimensional model file obtained in the previous step is stored in the three-dimensional model database; a model data set for training and identification in the three-dimensional model database is read; the read model data set is input to a three-dimensional model character extraction module to extract the color character of the three-dimensional model; the character data in color is read from a model character database; a quantification sensor of the color character is established; a triad color character vector representing the color character of the three-dimensional object is obtained; an analytic module of the neural network is established; the parameters of the network are adjusted; decision-making operation of the color character vector is carried out through the neural network; and according to a discrimination formula of the neural network, an identification result is output. The method can effectively classify the object colors with different classes.

Description

technical field

[0001] The invention belongs to the field of computer application technology, and in particular relates to a method for classifying color visual features of a three-dimensional model based on a neural network. Background technique

[0002] In recent years, with the development of computer graphics and the improvement of 3D model acquisition technology and graphics hardware technology, 3D model has become the fourth multimedia data type after sound, image and video. How to quickly retrieve the required 3D model is becoming another hot topic in the field of multimedia information retrieval. The content-based 3D model retrieval system uses the content information that reflects the visual characteristics of the 3D model, such as shape, spatial relationship, color, and material texture, to automatically calculate and extract the features of the 3D model, establish a multi-dimensional feature information index, and then in the multi-dimensional feature space Match...

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