Situation-Oriented Hierarchical Classification for Sightseeing Images Based on Local Color Feature

Nowadays, tourists take lots of photos and share them on album websites, so the meaningful grouping of images becomes important and useful. Specifically, sightseeing scenes vary with different situations such as weather and season. The categorization of different situations is thus expected to be beneficial to tourists planning when to visit different places. This paper proposes a hierarchical classification method based on local color feature extraction from the designed region of interest (ROI) and K-means clustering to categorize sightseeing images into several meaningful situations. Hierarchical organization consists of three stages and four situations. In the first stage, night-time images are discriminated from daytime images, then daytime images are divided into sunrise/sunset and other images in the second stage. Finally, cloudy images are separated from sunshiny images in other images obtained in the second stage. Experimental results show that the extraction of color features within the ROI is effective in obtaining clusters with high precision and recall.