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Animal Recognition Using Joint Visual Vocabulary
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2009 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

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,

KTH-animals

, containing

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

on

KTH-animals

and showed how it failed to encode the large variations in

this dataset.

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

fuzzy classifiers

we analyze

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

other with

fuzzy classifiers

. 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.

Applying

different model representation of different complexity within the same

classification framework, simple probabilistic models and more complex ones

based on Support Vector Machines.

Place, publisher, year, edition, pages
2009. , 55 p.
Series
TRITA-CSC-E, ISSN 1653-5715 ; 2009: 012
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-70069ISRN: KTH/CSC/E--09/012--SEOAI: oai:DiVA.org:kth-70069DiVA: diva2:485844
Uppsok
Technology
Supervisors
Examiners
Available from: 2013-12-13 Created: 2012-01-30 Last updated: 2013-12-13Bibliographically approved

Open Access in DiVA

No full text

Other links

http://www.csc.kth.se

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Maboudi Afkham, Heydar
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