Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE credits
This thesis presents a series of experiments on recognizing animals in complex
scenes. Unlike usual objects used for the recognition task (cars, airplanes, ...)
animals appear in a variety of poses and shapes in outdoor images. To perform
this task a dataset of outdoor images should be provided. Among the available
datasets there are some animal classes but as discussed in this thesis these
datasets do not capture the necessary variations needed for realistic analysis.
To overcome this problem a new extensive dataset,
realistic images of animals in complex natural environments. The methods
designed on the other datasets do not preform well on the animals dataset
due to the larger variations in this dataset. One of the methods that showed
promising results on one of these datasets on the animals dataset was applied
and showed how it failed to encode the large variations in
To familiarize the reader with the concept of computer vision and the
mathematics backgrounds a chapter of this thesis is dedicated to this matter.
This section presents a brief review of the texture descriptors and several
classification methods together with mathematical and statistical algorithms
needed by them.
To analyze the images of the dataset two different methodologies are introduced
in this thesis. In the first methodology
the images solely based on the animals skin texture of the animals. To do so an
accurate manual segmentation of the images is provided. Here the skin texture
is judged using many different features and the results are combined with each
. Since the assumption of neglecting the background
information in unrealistic the joint visual vocabularies are introduced.
Joint visual vocabularies
is a method for visual object categorization based
on encoding the joint textural information in objects and the surrounding background,
and requiring no segmentation during recognition. The framework can
be used together with various learning techniques and model representations.
Here we use this framework with simple probabilistic models and more complex
representations obtained using Support Vector Machines. We prove that
our approach provides good recognition performance for complex problems
for which some of the existing methods have difficulties.
The achievements of this thesis are a challenging database for animal
recognition. A review of the previous work and related mathematical background.
Texture feature evaluation on the "KTH-animal" dataset. Introduction
a method for object recognition based on joint statistics over the image.
different model representation of different complexity within the same
classification framework, simple probabilistic models and more complex ones
based on Support Vector Machines.
2009. , 55 p.
Carlsson, Stefan H. Å., Professor