Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper TuAT8.8
Background Filtering for Improving of Object Detection in Images
Qin, Ge, Univ. of Surrey
Vrusias, Bogdan, Univ. of Surrey
Gillam, Lee, Univ. of Surrey
We propose a method for improving object recognition in street scene images by identifying and filtering out background
aspects. We analyse the semantic relationships between foreground and background objects and use the information obtained
to remove areas of the image that are misclassified as foreground objects. We show that such background filtering
improves the performance of four traditional object recognition methods by over 40%. Our method is independent of the
recognition algorithms used for individual objects, and can be extended to generic object recognition in other environments
by adapting other object models.
09:00-11:10, Paper TuAT8.9
Sparse Local Discriminant Projections for Feature Extraction
Lai, Zhihui, Nanjing Univ. of Science and Tech.
Jin, Zhong, Nanjing Univ. of Science and Tech.
Yang, Jian, Nanjing Univ. of Science and Tech.
Wong, W.K., The Hong Kong Pol. Univ.
One of the major disadvantages of the linear dimensionality reduction algorithms, such as Principle Component Analysis
(PCA) and Linear Discriminant Analysis (LDA), are that the projections are linear combination of all the original features
or variables and all weights in the linear combination known as loadings are typically non-zero. Thus, they lack physical
interpretation in many applications. In this paper, we propose a novel supervised learning method called Sparse Local
Discriminant Projections (SLDP) for linear dimensionality reduction. SLDP introduces a sparse constraint into the objective
function and obtains a set of sparse projective axes with directly physical interpretation. The sparse projections can be efficiently
computed by the Elastic Net combining with spectral analysis. The experimental results show that SLDP give
the explicit interpretation on its projections and achieves competitive performance compared with some dimensionality
reduction techniques.
09:00-11:10, Paper TuAT8.10
Information-Theoretic Feature Selection from Unattributed Graphs
Bonev, Boyan, Univ. of Alicante
Escolano, Francisco, Univ. of Alicante
Giorgi, Daniela, National Res. Council
Biasotti, Silvia, CNR – IMATI
In this work we evaluate purely structural graph measures for 3D objects classification. We extract spectral features from
different Reeb graph representations. Information-theoretic feature selection gives an insight on which are the most relevant
features.
09:00-11:10, Paper TuAT8.11
Head Pose Estimation based on Random Forests for Multiclass Classification
Huang, Chen, Tsinghua Univ.
Ding, Xiaoqing, Tsinghua Univ.
Fang, Chi, Tsinghua Univ.
Head pose estimation remains a unique challenge for computer vision system due to identity variation, illumination
changes, noise, etc. Previous statistical approaches like PCA, linear discriminative analysis (LDA) and machine learning
methods, including SVM and Adaboost, cannot achieve both accuracy and robustness that well. In this paper, we propose
to use Gabor feature based random forests as the classification technique since they naturally handle such multi-class classification
problem and are accurate and fast. The two sources of randomness, random inputs and random features, make
random forests robust and able to deal with large feature spaces. Besides, we implement LDA as the node test to improve
the discriminative power of individual trees in the forest, with each node generating both constant and variant number of
children nodes. Experiments are carried out on two public databases to show the proposed algorithm outperforms other
approaches in both accuracy and computational efficiency.
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