Similar Seasonal-Geo-Region Mining Based on Visual Concepts in Social Media Photos

In this paper, we propose a method for similar geo-region mining focusing on the seasonality. We define "seasonal-geo-region" as a geographically and temporally continuous area where many travelers share common targets of interest. In order to extract such targets of interest, we consider that observing people's interests through the contents of social media photographs is an effective way. We first introduce a clustering method to decide seasonal-geo-region boundaries based on the geo-tag and shooting time accompanying a photo. Next, we introduce the proposed method that compares the similarity of a given pair of seasonal-geo-regions based on the likelihood distribution of Visual Concepts that appear in photographs belonging to each seasonal-geo-region. In the end, we introduce results of the seasonal-geo-region mining experiment and report an evaluation on the part of the results through a subjective experiment. The results showed the effectiveness of the proposed method.