Abstract. Image geolocalization is the task of identifying the location
depicted in a photo based only on its visual information. This task
is inherently challenging since many photos have only few, possibly
ambiguous cues to their geolocation. Recent work has cast this task as a
classification problem by partitioning the earth into a set of discrete cells
that correspond to geographic regions. The granularity of this partitioning
presents a critical trade-off; using fewer but larger cells results in lower
location accuracy while using more but smaller cells reduces the number
of training examples per class and increases model size, making the model
prone to overfitting. To tackle this issue, we propose a simple but effective
algorithm, combinatorial partitioning, which generates a large number
of fine-grained output classes by intersecting multiple coarse-grained
partitionings of the earth. Each classifier votes for the fine-grained classes
that overlap with their respective coarse-grained ones. This technique
allows us to predict locations at a fine scale while maintaining sufficient
training examples per class. Our algorithm achieves the state-of-the-art
performance in location recognition on multiple benchmark datasets