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Segmentation of Image Sequence into Scene-Coherent Parts

Sjöberg, Johan LU (2015) In Master's Theses in Mathematical Sciences FMA820 20141
Mathematics (Faculty of Engineering)
Abstract
The Narrative Clip is a small wearable camera that takes an image
every 30 seconds. By wearing the clip a whole day a user captures a
long image sequence of the day's events. In this thesis we will segment
such a sequence into the individual events automatically.
Multiple sequences are segmented by humans in order to nd a
groundtruth for each sequence. The groundtruth will be used to determine
the performance of the algorithm and also how well individual
humans are able to segment a sequence.
The method presented here takes a sequence of images and tries
to nd the location where the mean of the descriptors changes. The
images are described using various image descriptors that capture colors,
lines, textures and similar low level... (More)
The Narrative Clip is a small wearable camera that takes an image
every 30 seconds. By wearing the clip a whole day a user captures a
long image sequence of the day's events. In this thesis we will segment
such a sequence into the individual events automatically.
Multiple sequences are segmented by humans in order to nd a
groundtruth for each sequence. The groundtruth will be used to determine
the performance of the algorithm and also how well individual
humans are able to segment a sequence.
The method presented here takes a sequence of images and tries
to nd the location where the mean of the descriptors changes. The
images are described using various image descriptors that capture colors,
lines, textures and similar low level features. We also introduce
an indoor/outdoor classication method that combines a SVM and a
HMM. The classication method is combined with the segmentation
for each descriptor in order to create a combined segmentation.
The indoor classication method achieves an accuracy of 97% which
is to be considered very good results. The best human segmentation
has an F1-score of 0.82 while the best automatic segmentation
method's F1-score is 0.43. The conclusion is that the current system
is not suitable for any practical usage. (Less)
Please use this url to cite or link to this publication:
author
Sjöberg, Johan LU
supervisor
organization
course
FMA820 20141
year
type
H2 - Master's Degree (Two Years)
subject
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3268-2014
ISSN
1404-6342
other publication id
2014:E63
language
English
id
4934479
date added to LUP
2015-01-26 13:09:07
date last changed
2015-01-26 13:17:50
@misc{4934479,
  abstract     = {The Narrative Clip is a small wearable camera that takes an image
every 30 seconds. By wearing the clip a whole day a user captures a
long image sequence of the day's events. In this thesis we will segment
such a sequence into the individual events automatically.
Multiple sequences are segmented by humans in order to nd a
groundtruth for each sequence. The groundtruth will be used to determine
the performance of the algorithm and also how well individual
humans are able to segment a sequence.
The method presented here takes a sequence of images and tries
to nd the location where the mean of the descriptors changes. The
images are described using various image descriptors that capture colors,
lines, textures and similar low level features. We also introduce
an indoor/outdoor classication method that combines a SVM and a
HMM. The classication method is combined with the segmentation
for each descriptor in order to create a combined segmentation.
The indoor classication method achieves an accuracy of 97% which
is to be considered very good results. The best human segmentation
has an F1-score of 0.82 while the best automatic segmentation
method's F1-score is 0.43. The conclusion is that the current system
is not suitable for any practical usage.},
  author       = {Sjöberg, Johan},
  issn         = {1404-6342},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {Segmentation of Image Sequence into Scene-Coherent Parts},
  year         = {2015},
}