| Abstract: |  This thesis focuses on the problem of large scale visual object detection and classification
in digital images. A new type of image features that are derived from state-of-the-art
convolutional neural networks is proposed. It is further shown that the newly proposed
image signatures bare a strong resemblance to the Fisher Kernel classifier, that recently
became popular in the object category retrieval field. Because this new method suffers
from having a large memory footprint, several feature compression / selection techniques
are evaluated and their performance is reported. The result is an image classifier that
is able to surpass the performance of the original convolutional neural network, from
which it was derived. The new feature extraction method is also used for the object
detection task with similar results.
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