Hybrid Approach of Situation-Oriented Classification of Sightseeing Spot Images Based on Visual and Tag Information

Recent trend on the web is to share their traveling experience via uploading photos to web albums. Shared photos of sightseeing spots are important resources for those who are going to visit there. As sightseeing spot scenes vary with different situations, such as weather and season, automatic classification of photos into different situations is expected to be beneficial for tourists to plan when to visit there. This paper proposes a hybrid approach of combining content-based image classification with filtering based on tag information of image. By using geotag information when retrieving images from web albums, collected images can be limited to a reasonable boundary to eliminate outliers. Content-based image classification groups collected images into night, sunrise/sunset, cloudy, and shine situations. Moreover, by using the timestamp of images, the four situation categories are further verified to increase the accuracy. Experimental results show that the hybrid approach of content-based image classification and tag-based filtering is effective for classifying image into situations with high precision and recall.