Data-driven approach to learning salience models of indoor landmarks by using genetic programming

Hu, Xuke; Ding, Lei; Shang, Jing; Fan, Hongchao; Novack, Tessio; Noskov, Alexey; Zipf, Alexander

doi:10.1080/17538947.2019.1701109

Cited by 7 publications

(11 citation statements)

References 20 publications

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“…Recently, few studies have started to develop computational models to identify landmarks from indoor environments (Lyu et al 2015, Fellner et al 2017, Dubey et al 2019, Wang et al 2020, Hu et al 2020. These indoor models often adapted computational frameworks that were originally developed for outdoor landmark selection, and employed similar landmark salience measures and weighting/combination algorithms.…”

Section: Computational Indoor Landmark Selectionmentioning

confidence: 99%

“…Despite some novel techniques such as virtual reality (Dubey et al 2019) and eye-tracking (Wang et al 2020) for collecting indoor landmark data, the computational approaches for indoor landmark selection (Lyu et al 2015, Fellner et al 2017 still simply adapted the weighted linear models originally proposed for outdoor environments. Hu et al (2020) recently proposed a machine learning method based on genetic programming. However, their model only considered the visual and semantic salience of landmarks, and failed to include the structural salience, which has been found essential for landmark selection in many empirical studies (Klippel 2003, Kattenbeck 2015, Dubey et al 2019.…”

Section: Introductionmentioning

confidence: 99%

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Familiarity-dependent computational modelling of indoor landmark selection for route communication: a ranking approach

Zhou

Weibel

Huang

2021

International Journal of Geographical Information Science

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Landmarks play key roles in human wayfinding and mobile navigation systems. Existing computational landmark selection models mainly focus on outdoor environments, and aim to identify suitable landmarks for guiding users who are unfamiliar with a particular environment, and fail to consider familiar users. This study proposes a familiarity-dependent computational method for selecting suitable landmarks for communicating with familiar and unfamiliar users in indoor environments. A series of salience measures are proposed to quantify the characteristics of each indoor landmark candidate, which are then combined in two LambdaMART-based learning-to-rank models for selecting landmarks for familiar and unfamiliar users, respectively. The evaluation with labelled landmark preference data by human participants shows that people's familiarity with environments matters in the computational modelling of indoor landmark selection for guiding them. The proposed models outperform state-of-the-art models, and achieve hit rates of 0.737 and 0.786 for familiar and unfamiliar users, respectively. Furthermore, semantic relevance of a landmark candidate is the most important measure for the familiar model, while visual intensity is most informative for the unfamiliar model. This study enables the development of human-centered indoor navigation systems that provide familiarity-adaptive landmark-based navigation guidance.

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Section: Computational Indoor Landmark Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Familiarity-dependent computational modelling of indoor landmark selection for route communication: a ranking approach

Zhou

Weibel

Huang

2021

International Journal of Geographical Information Science

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“…Therefore, the landmarks play a central role in how information is acquired and, by extension, in the other cognitive processes of wayfinding. Because landmark-based wayfinding is known to be effective, landmarks should be included in route instructions [55,56]. Current studies focus mainly on the saliency of landmarks (e.g., [56][57][58][59]).…”

Section: Cognitive Processesmentioning

confidence: 99%

On the Right Track: Comfort and Confusion in Indoor Environments

Vanhaeren

Cock

Lapon

et al. 2020

IJGI

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Indoor navigation systems are not well adapted to the needs of their users. The route planning algorithms implemented in these systems are usually limited to shortest path calculations or derivatives, minimalizing Euclidian distance. Guiding people along routes that adhere better to their cognitive processes could ease wayfinding in indoor environments. This paper examines comfort and confusion perception during wayfinding by applying a mixed-method approach. The aforementioned method combined an exploratory focus group and a video-based online survey. From the discussions in the focus group, it could be concluded that indoor wayfinding must be considered at different levels: the local level and the global level. In the online survey, the focus was limited to the local level, i.e., local environmental characteristics. In this online study, the comfort and confusion ratings of multiple indoor navigation situations were analyzed. In general, the results indicate that open spaces and stairs need to be taken into account in the development of a more cognitively-sounding route planning algorithm. Implementing the results in a route planning algorithm could be a valuable improvement of indoor navigation support.

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“…In this paper, the AI method, GP algorithm, is utilized, since this algorithm offers a possibility of creating mathematical expression from the given data which provides the best correlation between input and output data. Over the years, GP has been implemented in various fields such as curve fitting, data modeling and symbolic regression [20][21][22][23]; image and signal processing [24][25][26][27]; financial trading, time series prediction and economic modeling [28][29][30][31]; and industrial process control [32][33][34][35]. However, GP has also been implemented in medicine-based tasks.…”

Section: Introductionmentioning

confidence: 99%

Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm

Anđelić

Šegota

Lorencin

et al. 2021

IJERPH

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Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is utilized to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is utilized on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22 January 2020–3 December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R2 score in the ranges 0.9406–0.9992, 0.9404–0.9998 and 0.9797–0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R2 score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy.

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Data-driven approach to learning salience models of indoor landmarks by using genetic programming

Cited by 7 publications

References 20 publications

Familiarity-dependent computational modelling of indoor landmark selection for route communication: a ranking approach

Familiarity-dependent computational modelling of indoor landmark selection for route communication: a ranking approach

On the Right Track: Comfort and Confusion in Indoor Environments

Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm

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