Abstract book (pdf) - ICPR 2010

correcting output code matrix. In this paper, we apply a decoding methodology in multiclass learning problems, in which
class labels of testing samples are unknown. In other words, without comparing the predicted and actual class labels, it
can be known whether testing samples are classified correctly. Based on this property, a new cascade classifier is introduced.
The classifier can improve the accuracy and will not result in over fitting. The analytical results show feasibility, accuracy,
and the advantages of the proposed method.
13:30-16:30, Paper ThBCT8.23
EEG-Based Emotion Recognition using Self-Organizing Map for Boundary Detection
Khosrowabadi, Reza, Nanyang Tech. Univ. Singapore
Ang, Kai Keng, Inst. for Infocomm Res. A*STAR
Quek, Hiok Chai, Nanyang Tech. Univ.
Bin Abdul Rahman, Abdul Wahab, International Islamic Univ. Malaysia
This paper presents an EEG-based emotion recognition system using self-organizing map for boundary detection. Features
from EEG signals are classified by considering the subjects‘ emotional responses using scores from SAM questionnaire.
The selection of appropriate threshold levels for arousal and valence is critical to the performance of the recognition system.
Therefore, this paper investigates the performance of a proposed EEG-based emotion recognition system that employed selforganizing
map to identify the boundaries between separable regions. A study was performed to collect 8 channels of EEG
data from 26 healthy right-handed subjects in experiencing 4 emotional states while exposed to audio-visual emotional
stimuli. EEG features were extracted using the magnitude squared coherence of the EEG signals. The boundaries of the EEG
features were then extracted using SOM. 5-fold cross-validation was then performed using the k-nn classifier. The results
showed that proposed method improved the accuracies to 84.5%.
13:30-16:30, Paper ThBCT8.24
Vocabulary-Based Approaches for Multiple-Instance Data: A Comparative Study
Amores, Jaume, Univ. Autònoma de Barcelona
Multiple Instance Learning (MIL) has become a hot topic and many different algorithms have been proposed in the last
years. Despite this fact, there is a lack of comparative studies that shed light into the characteristics of the different methods
and their behavior in different scenarios. In this paper we provide such an analysis. We include methods from different families,
and pay special attention to vocabulary-based approaches, a new family of methods that has not received much attention
in the MIL literature. The empirical comparison includes seven databases from four heterogeneous domains, implementations
of eight popular MIL methods, and a study of the behavior under synthetic conditions. Based on this analysis, we show that,
with an appropriate implementation, vocabulary-based approaches outperform other MIL methods in most of the cases,
showing in general a more consistent performance.
13:30-16:30, Paper ThBCT8.25
A Multiple Classifier System Approach for Facial Expressions in Image Sequences Utilizing GMM Supervectors
Schels, Martin, Univ. of Ulm
Schwenker, Friedhelm, Univ. of Ulm
The Gaussian mixture model (GMM) super vector approach is a well known technique in the domain of speech processing,
e.g. speaker verification and audio segmentation. In this paper we apply this approach to video data in order to recognize
human facial expressions. Three different image feature types (optical flow histograms, orientation histograms and principal
components) from four pre-selected regions of the human’s face image were extracted and GMM super-vectors of the feature
channels per sequence were constructed. Support vector machines (SVM) were trained using these super vectors for every
channel separately and its results were combined using classifier fusion techniques. Thus, the performance of the classifier
could be improved compared to the best individual classifier.
13:30-16:30, Paper ThBCT8.26
Incremental Learning of Visual Landmarks for Mobile Robotics
Bandera, Antonio, Univ. of Malaga
Vázquez-Martín, Ricardo, Centro Andaluz de Innovación y Tecnologías de la Información y las Comunicaciones CITIC
Marfil, Rebeca, Univ. of Malaga
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