Construction of a Visual Saliency Model for Neighborhood Building Landmarks Based on K‐Means Clustering

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.

Using the big data analysis and basic information from lecture Halls to predict air change rate

“…It is necessary to import Principal Component Analysis (PCA) for dimensionality reduction, find out the main principal components, and get more information through clustering to predict better performance [ 28 , 29 ]. Common clustering methods include K-means [ 30 ], Density-based spatial clustering of applications with noise (DBSCAN) [ 31 ], Gaussian Mixture Model (GMM) [ 32 ], which can effectively overcome the problem of excessive differences in building materials. For example, Rezaeian, et al [ 33 ] used different natural ventilation articles for cluster analysis, Wang, et al [ 34 ] used K-means to find two unclustered wind speed variations, Bienvenido-Huertas, et al [ 35 ] used natural ventilation cluster to improve the problem of energy shortage in coastal areas.…”