Task-Driven Image Retrieval Using Geographic Information

When large-scale online geo-tagged images come into view, it is important to leverage geographic information for web image retrieval. In this paper, a geo-metadata based image retrieval system is proposed using both textual tags and visual features. This image retrieval system is especially useful for tourism related tasks such as tourism recommendation and tourism guide. First, the requested image retrieval task is classified into one of the three different types according to the retrieval purpose, and then it can be handled with specific method. Second, a WordNet hierarchy based semantic similarity is developed to measure the similarity between different cities. This semantic similarity is somehow consistent with the visual similarity. Finally, a high-level image representation method is proposed to narrow the semantic gap between the low-level visual features and high-level image concepts. The proposed algorithm is evaluated on an image set which is consisted of totally 177,158 images of 120 most popular cities all over the world collected from Flickr, and the experiments have provided very positive results.