Abstract book (pdf) - ICPR 2010

In this paper, we propose a visual tracking approach based on “bag of features” (BoF) algorithm. We randomly sample
image patches within the object region in training frames for constructing two codebooks using RGB and LBP features,
instead of only one codebook in traditional BoF. Tracking is accomplished by searching for the highest similarity between
candidates and codebooks. Besides, updating mechanism and result refinement scheme are included in BoF tracking. We
fuse patch-based approach and global template-based approach into a unified framework. Experiments demonstrate that
our approach is robust in handling occlusion, scaling and rotation.
16:50-17:10, Paper MoBT1.5
Gradient Constraints Can Improve Displacement Expert Performance
Tresadern, Philip Andrew, Univ. of Manchester
Cootes, Tim, The Univ. of Manchester
The `displacement expert’ has recently proven popular for rapid tracking applications. In this paper, we note that experts
are typically constrained only to produce approximately correct parameter updates at training locations. However, we
show that incorporating constraints on the gradient of the displacement field within the learning framework results in an
expert with better convergence and fewer local minima. We demonstrate this proposal for facial feature localization in
static images and object tracking over a sequence.
MoBT2 Topkapı Hall B
Dimensionality Reduction Regular Session
Session chair: Somol, Petr (Institute of Information Theory and Automation)
15:30-15:50, Paper MoBT2.1
Temporal Extension of Laplacian Eigenmaps for Unsupervised Dimensionality Reduction of Time Series
Lewandowski, Michal, Kingston Univ.
Martinez-Del-Rincon, Jesus, Kingston Univ.
Makris, Dimitrios, Kingston Univ.
Nebel, Jean-Christophe, Kingston Univ.
A novel non-linear dimensionality reduction method, called Temporal Laplacian Eigenmaps, is introduced to process efficiently
time series data. In this embedded-based approach, temporal information is intrinsic to the objective function,
which produces description of low dimensional spaces with time coherence between data points. Since the proposed
scheme also includes bidirectional mapping between data and embedded spaces and automatic tuning of key parameters,
it offers the same benefits as mapping-based approaches. Experiments on a couple of computer vision applications demonstrate
the superiority of the new approach to other dimensionality reduction method in term of accuracy. Moreover, its
lower computational cost and generalisation abilities suggest it is scalable to larger datasets.
15:50-16:10, Paper MoBT2.2
Orthogonal Locality Sensitive Fuzzy Discriminant Analysis in Sleep-Stage Scoring
Khushaba, Rami N., Univ. of Tech. Sydney
Elliott, Rosalind, Univ. of Tech. Sydney
Alsukker, Akram, Univ. of Tech. Sydney
Al-Ani, Ahmed, Univ. of Tech. Sydney
Mckinley, Sharon, Univ. of Tech. Sydney
Sleep-stage scoring plays an important role in analyzing the sleep patterns of people. Studies have revealed that Intensive
Care Unit (ICU) patients do not usually get enough quality sleep, and hence, analyzing their sleep patterns is of increased
importance. Due to the fact that sleep data are usually collected from a number of Electroencephalogram (EEG), Electromyogram
(EMG) and Electrooculography (EOG) channels, the feature set size can become large, which may affect the
development of on-line scoring systems. Hence, a dimensionality reduction step is needed. One of the powerful dimensionality
reduction approaches is based on the concept of Linear Discriminant Analysis (LDA). Unlike existing variants
of LDA, this paper presents a new method that considers the fuzzy nature of input measurements while preserving their
local structure. Practical results indicate the significance of preserving the local structure of sleep data, which is achieved
by the proposed method, and hence attaining superior results to other dimensionality reduction methods.
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