Research Of Mining And Visualization Of Tour Information On Internet

In this thesis, API of Flickr is implemented to obtain images and their corresponding metadata (such as the image ID, uploading time, the time the images taken photograph), the geo tags and annotated textual tags.After getting abovementioned information, we use k-means and AP algorithms together to group similar images within neighboring spatial distances. In order to find visually similar images within each group, BBF(Best-Bin First) algorithm is conducted to align each images by SIFT local features. Then, WTF-IDF-UF is conducted to obtain informative tags for each group which contain visually similar images with neighbored distance, and the Naive Scan Methods are used to remove random irrelevant tags. At last, we can get the name, location, popularity of the hot landmarks as well as the representative images for each image group.In order to better store each image group and their corresponding information, a global quadtree is introduced in this thesis. The basic idea of global quadtree is to segment the map into different squares and map any location in the map to a quadcode at any levels.at last we use Google maps API and AJAX to show the information intuitively and dynamically.