Geolocation based landmark detection and annotation-towards clickable real world-

Clickable Real World is a new framework to realize an intuitive information search with a mobile terminal. To achieve the goal, we tackle two challenging tasks. One is landmark detection from an observing scene. Our approach detects a landmark based on an image prior. The prior is not given manually. Instead, it is generated automatically from the training samples collected from photo sharing website. Another challenging task is image annotation assisted by geolocation. We use the location of a user who uses a mobile terminal, and geolocation where the training sample images were taken. Two probabilistic models are generated to achieve image annotation. One is image-based labeling which utilizes the co-occurrence between image features and label features. The other is label-based localization which uses the consensus about the label given around the geolocation among photographers. We combine two probabilistic approaches to improve the accuracy of image annotation. We demonstrate this approach for 87 scenes in the world.