B Olshausen

M Milanova

Mariofanna Milanova

U Rutishauser

BMC Bioinformatics

English

PubMed

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Model of visual attention for video sequences

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BioMed CentralBMC Bioinformatics
ssOpen AccePoster presentation
Model of visual attention for video sequences
Mariofanna Milanova
Address: Department of Computer Science, University of Arkansas at Little Rock, Little Rock, AR 72204, USA
Email: Mariofanna Milanova - mgmilanova@ualr.edu
Background
In our previous work [1] we generalized Olshausen's algo-
rithm [2] and designed a perceptual learning model using
video sequences. In this study we propose to model con-
junction search. Conjunction search (search for a unique
combination of two features – e.g., orientation and spatial
frequency – among distractions that share only one of
these features) examines how the system combines fea-
tures into perceptual wholes. We propose to improve the
effectiveness of the decomposition algorithm by provid-
ing classification awareness. Attentional guidance does
not depend solely on local visual features, but must also
include the effects of interactions among features. The
idea is to group together filters that will be responsible to
extract similar features. It is well known that knowledge
about which features define the target improves search
performance and/or accuracy [3]. The nearest neighbors
of the fixations will share a certain feature.
Methods
The main goal of this work is to use sequences of images
and to design an attention model including conjunction
search based on unsupervised self-learning. First, Inde-
pendent Component Analysis algorithm is used to deter-
mine an initial set of basis functions from the first image
(Figure 1). Second, unsupervised self-organizing learning
is used to group together similar bases functions (Figure
2).
Conclusion
It is shown that performing sparse learning codes on video
sequences of natural scenes produces results with qualita-
tively similar spatio-temporal properties of simple recep-
tive field of neurons. The basic functions are similar to
those obtained by sparse learning, but in our model they
have a particular order (Figure 3). The proposed frame-
work was tested using neurobiological (event related
potentials ERP's) and behavioral (eye tracking) data.
from UT-ORNL-KBRIN Bioinformatics Summit 2008
Cadiz, KY, USA. 28–30 March 2008
Published: 8 July 2008
BMC Bioinformatics 2008, 9(Suppl 7):P14 doi:10.1186/1471-2105-9-S7-P14
<supplement> <title> <p>UT-ORNL-KBRIN Bioinformatics Summit 2008</p> </title> <editor>Eric C Rouchka and Julia Krushkal</editor> <note>Meeting abstracts – A single PDF containing all abstracts in this Supplement is available <a href="http://www.biomedcentral.com/content/pdf/1471-2105-9-S7-full.pdf">here</a>.</note> <url>http://www.kbrin.louisville.edu/summit/</url> </supplement>
This abstract is available from: http://www.biomedcentral.com/1471-2105/9/S7/P14
© 2008 Milanova; licensee BioMed Central Ltd. 
Initial basis imagesFigure 1
Initial basis images. a) the original image; b) a set of basis functions received by ICA from several patches from one image; c) 
and d) the convolution results of two functions with the original image.Page 1 of 2
(page number not for citation purposes)
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Acknowledgements
The project described was supported by NIH Grant Number P20 RR-
16460 from the IDeA Networks of Biomedical Research Excellence 
(INBRE) Program of the National Center for Research Resources.
References
1. Milanova M, Wachowiak M, Rubin S, Elmaghraby A: A perceptual
learning model based on topological representation, neural
networks.  Proceedings, IJCNN'01 International Conference
2001:406-411.
2. Olshausen B: Sparse codes and spikes.  In Probabilistic Models of
Perception and Brain Function Edited by: Rao RPN, Olshausen BA, Lewicki
MS. MIT Press; 2001. 
3. Rutishauser U, Koch C: Probabilistic modeling of eye move-
ment data during conjunction search via feature-based
attention.  Journal of Vision 2007, 7(6):5. 1–20
Ordered resulting basis functionsFigur  3
Ordered resulting basis functions.
Components for unsupervised self-organizing learningFigure 2
Components for unsupervised self-organizing learning.Page 2 of 2
(page number not for citation purposes)


Elmaghraby A: A perceptual learning model based on topological representation, neural networks.

Probabilistic modeling of eye movement data during conjunction search via feature-based attention.

Sparse codes and spikes. In Probabilistic Models of Perception and Brain Function Edited by: Rao RPN, Olshausen

https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-9-S7-P14?site=bmcbioinformatics.biomedcentral.com

Model of visual attention for video sequences

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