With the digitalization of capturing images, the amount of images drasti-
cally increased and searching through a collection of images became very
hard. This dissertation deals with querying a collection of images based on a
reference image. We use a modern approach based on features obtained from
a deep model based on a convolutional neural network. Such features are not
sparse and we cannot build inverted indexes with them. In our approach we
use hierarchical clustering with a conditional density tree for querying. We
build a prototype of an image search service with the tree structure which
is able to responsively serve multiple users at the same time. We test the
solution against a brute force approach and find that the suggested method
is more suited for large collections of images, as it consumes less memory and
needs less time for queries.
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