  Due to the increasing need for effective security measures and the
integration of cameras in commercial products, a hugeamount of visual data is
created today. Law enforcement agencies (LEAs) are inspecting images and videos
to findradicalization, propaganda for terrorist organizations and illegal
products on darknet markets. This is time consuming.Instead of an undirected
search, LEAs would like to adapt to new crimes and threats, and focus only on
data from specificlocations, persons or objects, which requires flexible
interpretation of image content. Visual concept detection with
deepconvolutional neural networks (CNNs) is a crucial component to understand
the image content. This paper has fivecontributions. The first contribution
allows image-based geo-localization to estimate the origin of an image. CNNs
andgeotagged images are used to create a model that determines the location of
an image by its pixel values. The secondcontribution enables analysis of
fine-grained concepts to distinguish sub-categories in a generic concept. The
proposedmethod encompasses data acquisition and cleaning and concept
hierarchies. The third contribution is the recognition ofperson attributes
(e.g., glasses or moustache) to enable query by textual description for a
person. The person-attributeproblem is treated as a specific sub-task of
concept classification. The fourth contribution is an intuitive image
annotationtool based on active learning. Active learning allows users to define
novel concepts flexibly and train CNNs with minimalannotation effort. The fifth
contribution increases the flexibility for LEAs in the query definition by
using query expansion.Query expansion maps user queries to known and detectable
concepts. Therefore, no prior knowledge of the detectableconcepts is required
for the users. The methods are validated on data with varying locations
(popular and non-touristiclocations), varying person attributes (CelebA
dataset), and varying number of annotations.
