  Many people take photos and videos with smartphones and more recently with
360-degree cameras at popular places and events, and share them in social
media. Such visual content is produced in large volumes in urban areas, and it
is a source of information that online users could exploit to learn what has
got the interest of the general public on the streets of the cities where they
live or plan to visit. A key step to providing users with that information is
to identify the most popular k spots in specified areas. In this paper, we
propose a clustering and incremental sampling (C&IS) approach that trades off
accuracy of top-k results for detection speed. It uses clustering to determine
areas with high density of visual content, and incremental sampling, controlled
by stopping criteria, to limit the amount of computational work. It leverages
spatial metadata, which represent the scenes in the visual content, to rapidly
detect the hotspots, and uses a recently proposed Gaussian probability model to
describe the capture intention distribution in the query area. We evaluate the
approach with metadata, derived from a non-synthetic, user-generated dataset,
for regular mobile and 360-degree visual content. Our results show that the
C&IS approach offers 2.8x-19x reductions in processing time over an optimized
baseline, while in most cases correctly identifying 4 out of 5 top locations.
