Abstract
Regarding the restriction of the wood processing enterprises in the market, intelligent artificial wood materials are mainly based on the demand for pattern quality levels, and the calculation method of multimedia resource theme search is used to achieve the pattern design of intelligent auxiliary artificial wood materials. First, analyze the pattern characteristics of intelligent auxiliary artificial wood materials. After analyzing the characteristics, use the multimedia resource subject search calculation method to carry out the binarization design. At the same time, use the self-learning method to optimize the convergence efficiency and reduce the design time. Finally, pass the softmax designer extracts design schemes for patterns and straight lines.
Highlights
When processing wood, the important factor that determines the quality of wood is the pattern of wood. e use value and commercial value of wood and finished products mainly depend on the quality of wood. e utilization rate of domestic raw material wood is relatively low, accounting for only 63%, and the comprehensive utilization rate of foreign wood raw materials has reached 80%, which is a big difference [1,2,3], mainly because of the low efficiency of wood patterns
According to the general rule of the multimedia resource topic search algorithm, the first group is established based on the parameter coding, and the genetic operation is performed, and the quasigroup is stopped by the genetic algebra limit
Wood Pattern Design Based on the Multimedia Resource Topic Search Algorithm and Self-Learning DBN
Summary
The important factor that determines the quality of wood is the pattern of wood. e use value and commercial value of wood and finished products mainly depend on the quality of wood. e utilization rate of domestic raw material wood is relatively low, accounting for only 63%, and the comprehensive utilization rate of foreign wood raw materials has reached 80%, which is a big difference [1,2,3], mainly because of the low efficiency of wood patterns. Ose shallow learning methods that detect wood patterns are mostly subjected to complex processing tasks, such as image preoperation, segmentation, feature analysis, pattern recognition, edge detection, and other works, but the recognition is not accurate and cannot be engaged in the more cumbersome wood texture detection work. In response to this situation, the deep learning multimedia resource subject search calculation is added to the wood pattern work, and the multimedia resource subject search calculation extraction characteristics and self-learning methods are used at the same time to reduce the design time based on the learning of the priority calculation characteristics. Network multimedia resources mean multimedia resources on the Internet, including images, sounds, videos, animations, and so on [7,8,9]. e theme search system for network multimedia resources is mainly designed. e theme
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