Abstract
In this paper, firstly, based on the quantitative relationship between K-means clustering and visual saliency of neighborhood building landmarks, the weights occupied by each index of composite visual factors are obtained by using multiple statistical regression methods, and, finally, we try to construct a saliency model of multiple visual index composites and analyze and test the model. As regards decomposition and quantification of visual saliency influencing factors, to describe and quantify these visual significance factors of the landmarks, the significant factors are decomposed into several quantifiable secondary indicators. Considering that the visual saliency of the landmarks in the neighborhood is reflected by the variance of the influencing factors and that the scope of the landmarks is localized, the local outlier detection algorithm is used to solve the variance of the secondary indicators. Since the visual significance of neighborhood building landmarks is influenced by a combination of influencing factors, the overall difference degree of secondary indicators is calculated by K-means clustering. To facilitate the factor calculation, a factor-controlled virtual environment was built to carry out the experimental study of landmark perception and calculate the different degrees of each index of the building. The data of visual indicators of the neighborhood buildings for this experiment were also collected, and the significance values of the neighborhood buildings were calculated. The influence weights of the indicators were obtained by using multiple linear regression analysis, the visual significance model of the landmarks of the neighborhood buildings in the factor-controlled environment was constructed, and the model was analyzed and tested.
Highlights
Research ArticleReceived 15 July 2021; Revised 9 August 2021; Accepted 13 August 2021; Published 24 August 2021
In this paper, firstly, based on the quantitative relationship between K-means clustering and visual saliency of neighborhood building landmarks, the weights occupied by each index of composite visual factors are obtained by using multiple statistical regression methods, and, we try to construct a saliency model of multiple visual index composites and analyze and test the model
Since the visual significance of neighborhood building landmarks is influenced by a combination of influencing factors, the overall difference degree of secondary indicators is calculated by K-means clustering
Summary
Received 15 July 2021; Revised 9 August 2021; Accepted 13 August 2021; Published 24 August 2021. E influence weights of the indicators were obtained by using multiple linear regression analysis, the visual significance model of the landmarks of the neighborhood buildings in the factor-controlled environment was constructed, and the model was analyzed and tested. E main idea and framework of the study are to qualitatively select the factors affecting the visual salience of neighborhood building landmarks from the perspective of spatial objects and navigators and to describe, quantify, and calculate the degree of difference of the factors by combining the quantitative methods of related fields. Chapter three is dedicated to the study of the visual saliency model of neighborhood building landmarks based on K-means clustering. Multiple linear regression methods were used to obtain the influence weights of indicators, and, an attempt was made to construct a model of visual saliency of building landmarks in a factor-controlled environment, and the model was analyzed and multidimensionally tested. With the increasing building height and density, super high-rise buildings are like trees in the forest competing for sunlight, and the sunlight problem is becoming
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
