ABSTRACT

In landmark-based way-finding, determining the most salient landmark from several candidates at decision points is challenging. To overcome this problem, current approaches usually rely on a linear model to measure the salience of landmarks. However, linear models are not always able to establish an accurate quantitative relationship between the attributes of a landmark and its perceived salience. Furthermore, the numbers of evaluated scenes and of volunteers participating in the testing of these models are often limited. With the aim of overcoming these gaps, we propose learning a non-linear salience model by means of genetic programming. We compared our proposed approach with conventional algorithms by using photographs of two hundred test scenes collected from two shopping malls. Two hundred volunteers who were not in these environments were asked to answer questionnaires about the collected photographs. The 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 approach. This accuracy rate is considerably higher than the ones achieved by conventional linear models.

摘要

在基于地标的寻路中，从决策点的多个候选中确定最显着的地标具有挑战性。为了克服这个问题，当前的方法通常依赖于线性模型来测量地标的显着性。但是，线性模型并不总是能够在地标的属性与其感知的显着性之间建立准确的定量关系。此外，被评估的场景和参与测试这些模型的志愿者的数量通常是有限的。为了克服这些差距，我们建议通过遗传编程来学习非线性显着性模型。通过使用从两个购物中心收集的200个测试场景的照片，我们将建议的方法与常规算法进行了比较。要求不在这些环境中的200名志愿者回答有关所收集照片的问卷。该实验的结果表明，在76％的情况下，我们提出的方法可以正确预测最显着的地标（根据志愿者的感知）。该准确率大大高于传统线性模型所达到的准确率。