2018/03/24 by Ch. Md. Rakin Haider, Haider, Ch. Md. Rakin, Mohammed Eunus Ali +1
Computer Science · Social Sciences · #Computers and Society (cs.CY) #Diverse Aspects of Tourism Research #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Social and Information Networks (cs.SI) #Visual Attention and Saliency Detection
paper · pdf · doi:10.48550/arxiv.1804.03506
openalex publication_date 2018/03/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this work, we propose a novel technique to determine the aesthetic score\nof a location from social metadata of Flickr photos. In particular, we built\nmachine learning classifiers to predict the class of a location where each\nclass corresponds to a set of locations having equal aesthetic rating. These\nmodels are trained on two empirically build datasets containing locations in\ntwo different cities (Rome and Paris) where aesthetic ratings of locations were\ngathered from TripAdvisor.com. In this work we exploit the idea that in a\nlocation with higher aesthetic rating, it is more likely for an user to capture\na photo and other users are more likely to interact with that photo. Our models\nachieved as high as 79.48% accuracy (78.60% precision and 79.27% recall) on\nRome dataset and 73.78% accuracy(75.62% precision and 78.07% recall) on Paris\ndataset. The proposed technique can facilitate urban planning, tour planning\nand recommending aesthetically pleasing paths.\n