Data-driven approach to learning salience models of indoor landmarks by using genetic programming
由使用基因编程听说凸起模型室内的里程碑的数据驱动的途径作者机构：GIScience Research GroupInstitute of GeographyHeidelberg UniversityHeidelbergGermany School of Geography and Information EngineeringChina University of GeosciencesWuhanPeople’s Republic of China National Engineering Research Center for Geographic Information SystemWuhanPeople’s Republic of China Department of Civil and Environmental EngineeringNorwegian University of Science and TechnologyTrondheimNorway
出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)
年 卷 期：2020年第13卷第11期
页 面：1230-1257页
核心收录：
学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学]
基 金：the National Key R&D Program of China(No.2016YFB0502203) the National Natural Science Foundation of China(Grant No.41271440) the China Scholarship Council
主 题：Indoor navigation landmarks salience model genetic programming
摘 要：In landmark-based way-finding,determining the most salient landmark from several candidates at decision points is *** overcome this problem,current approaches usually rely on a linear model to measure the salience of ***,linear models are not always able to establish an accurate quantitative relationship between the attributes of a landmark and its perceived ***,the numbers of evaluated scenes and of volunteers participating in the testing of these models are often *** the aim of overcoming these gaps,we propose learning a non-linear salience model by means of genetic *** compared our proposed approach with conventional algorithms by using photographs of two hundred test scenes collected from two shopping *** hundred volunteers who were not in these environments were asked to answer questionnaires about the collected *** results from this experiment showed that in 76%of the cases,the most salient landmark(according to the volunteers’perception)was correctly predicted by our proposed *** accuracy rate is considerably higher than the ones achieved by conventional linear models.
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