Application of Image Understanding and Human-Computer Interaction Technology in Intelligent Assisted Travel Systems

To investigate the application effectiveness of image understanding and human-computer interaction technology in intelligent assisted travel systems, this paper combines Gaussian filtering and a multi-threshold local binary pattern algorithm to propose a multi-feature fusion image recognition algorithm, which serves as the basis for designing an intelligent assisted travel system. Data shows that when the original image information entropy ranges from 6 to 10 bits, the multi-feature fusion algorithm increases image information entropy by approximately 5.5 bits and improves recognition accuracy by 15.1% compared to traditional local binary pattern algorithms. In practical applications of the intelligent assisted travel system, user satisfaction scored 89.3 points, and system effectiveness scored 88.6 points. The results indicate that image understanding-based human-computer interaction technology demonstrates high recognition accuracy and user satisfaction within intelligent assisted travel, significantly enhancing travel experience and service quality, thus providing valuable theoretical and practical references for the field of intelligent assisted travel.