Abstract:
Techniques for generating cross-modality semantic classifiers and using those cross-modality semantic classifiers for ground level photo geo-location using digital elevation are provided. In one aspect, a method for generating cross-modality semantic classifiers is provided. The method includes the steps of: (a) using Geographic Information Service (GIS) data to label satellite images; (b) using the satellite images labeled with the GIS data as training data to generate semantic classifiers for a satellite modality; (c) using the GIS data to label Global Positioning System (GPS) tagged ground level photos; (d) using the GPS tagged ground level photos labeled with the GIS data as training data to generate semantic classifiers for a ground level photo modality, wherein the semantic classifiers for the satellite modality and the ground level photo modality are the cross-modality semantic classifiers.
Abstract:
Techniques for generating cross-modality semantic classifiers and using those cross-modality semantic classifiers for ground level photo geo-location using digital elevation are provided. In one aspect, a method for generating cross-modality semantic classifiers is provided. The method includes the steps of: (a) using Geographic Information Service (GIS) data to label satellite images; (b) using the satellite images labeled with the GIS data as training data to generate semantic classifiers for a satellite modality; (c) using the GIS data to label Global Positioning System (GPS) tagged ground level photos; (d) using the GPS tagged ground level photos labeled with the GIS data as training data to generate semantic classifiers for a ground level photo modality, wherein the semantic classifiers for the satellite modality and the ground level photo modality are the cross-modality semantic classifiers.
Abstract:
Techniques for spatial semantic attribute matching on image regions for location identification based on a reference dataset are provided. In one aspect, a method for matching images from heterogeneous sources is provided. The method includes the steps of: (a) parsing the images into different semantic labeled regions; (b) creating a list of potential matches by matching the images based on two or more of the images having same semantic labeled regions; and (c) pruning the list of potential matches created in step (b) by taking into consideration spatial arrangements of the semantic labeled regions in the images.
Abstract:
Techniques for spatial semantic attribute matching on image regions for location identification based on a reference dataset are provided. In one aspect, a method for matching images from heterogeneous sources is provided. The method includes the steps of: (a) parsing the images into different semantic labeled regions; (b) creating a list of potential matches by matching the images based on two or more of the images having same semantic labeled regions; and (c) pruning the list of potential matches created in step (b) by taking into consideration spatial arrangements of the semantic labeled regions in the images.