Hong-Mo Je

Pattern classification using support vector machine ensemble

While the support vector machine (SVM) can provide a good generalization performance, the classification result of the SVM is often far from the theoretically expected level in practical implementation because they are based on approximated algorithms due to the high complexity of time and space. To improve the limited classification performance of the real SVM, we propose to use an SVM ensemble with bagging (bootstrap aggregating) or boosting. In bagging, each individual SVM is trained independently, using randomly chosen training samples via a bootstrap technique. In boosting, each individual SVM is trained using training samples chosen according to the samples probability distribution, which is updated in proportion to the degree of error of the sample. In both bagging and boosting, the trained individual SVMs are aggregated to make a collective decision in several ways, such as majority voting, least squares estimation based weighting, and double-layer hierarchical combination. Various simulation results for handwritten digit recognition and fraud detection show that the proposed SVM ensemble with bagging or boosting greatly outperforms a single SVM in terms of classification accuracy.

Support Vector Machine Ensemble with Bagging

Even the support vector machine (SVM) has been proposed to provide a good generalization performance, the classification result of the practically implemented SVM is often far from the theoretically expected level because their implementations are based on the approximated algorithms due to the high complexity of time and space. To improve the limited classification performance of the real SVM, we propose to use the SVM ensembles with bagging (bootstrap aggregating). Each individual SVM is trained independently using the randomly chosen training samples via a bootstrap technique. Then, they are aggregated into to make a collective decision in several ways such as the majority voting, the LSE(least squares estimation)-based weighting, and the double-layer hierarchical combining. Various simulation results for the IRIS data classification and the hand-written digit recognitionshow that the proposed SVM ensembles with bagging outperforms a single SVM in terms of classification accuracy greatly.

The POSTECH face database (PF07) and performance evaluation

We constructed a face database POSTECH face database (PF07). PF07 contains the true-color face images of 200 people, 100 men and 100 women, representing 320 various images (5 pose variations times 4 expression variations times 16 illumination variations) per person. All of the people in the database are Korean. We also present the results of face recognition experiments under various conditions using three baseline face recognition algorithms in order to provide an example evaluation protocol on the database. The database is expected to be used to evaluate the algorithm of face recognition for Korean people or for people with systematic variations.

Hand Gesture Recognition To Understand Musical Conducting Action

This paper deals with the understanding of four musical time patterns and three tempos that are generated by a human conductor of robot orchestra or an operator of computer-based music play system using the hand gesture recognition. We use only a stereo vision camera with no extra special devices such as sensor glove, 3D motion capture system, infra-red camera, electronic baton and so on. We propose a simple and reliable vision-based hand gesture recognition using the conducting feature point (CFP), the motion-direction code, and the motion history matching. The proposed hand gesture recognition system operates as follows: First, it extracts the human hand region by segmenting the depth information generated by stereo matching of image sequences. Next, it follows the motion of the center of the gravity(COG) of the extracted hand region and generates the gesture features such as CFP and the direction-code. Finally, we obtain the current timing pattern of the musics beat and tempo by the proposed hand gesture recognition using either CFP tracking or motion histogram matching. The experimental results show that the musical time pattern and tempo recognition rate are over 86% on the test data set when the motion histogram matching is used.

Human Face Detection in Digital Video Using SVMEnsemble

This Letter proposes automatic human face detection in digital video using a support vector machine (SVM) ensemble to improve the detection performance. The SVM ensemble consists of several independently trained SVMs using randomly chosen training samples via a bootstrap technique. Next, they are aggregated in order to make a collective decision via a majority voting scheme. Experimental results show that the proposed face detection method using SVM ensemble outperforms conventional methods such as using only single SVM and Multi-Layer Perceptron in terms of classification accuracy, false alarms, and missing rates.

Partially observed distance mapping for cooperative multi-robot localization

This paper presents a distance mapping-based multi-robot localization method, which works with incomplete data. We make three contributions. First, we propose the use of multi dimensional scaling (MDS) for multi-robot localization. Second, we formulate the problem to accommodate partial observations common in multi-robot settings. We solve the resulting optimization problem using “scaling by majorizing a complicated function,” a popular algorithm for iterative MDS. Third, we take advantage of the motion information of robots to help the optimization procedure. Three policies are compared at each time step: random, previous, and prediction (constructed by combining the previous pose estimates with motion information). Using extensive empirical results, we show that the initialization by the prediction method results in better performance in terms of both accuracy and speed when compared to the other two initialization techniques.

Vision-Based Hand Gesture Recognition for Understanding Musical Time Pattern and Tempo

We introduce a method of understanding of four musical time patterns and three tempos that are generated by a human conductor of robot orchestra or an operator of computer- based music play system using the hand gesture recognition. We use only a stereo vision camera with no extra special devices. We suggest a simple and reliable vision-based hand gesture recognition with two naive features. One is the motion-direction code which is a quantized code for motion directions. The other is the conducting feature point (CFP) where the point of sudden motion changes. The proposed hand gesture recognition system operates as follows: First, it extracts the human hand region by segmenting the depth information generated by stereo matching of image sequences. Next, it follows the motion of the center of the gravity(COG) of the extracted hand region and generates the gesture features such as CFP and the direction-code. Finally, we obtain the current timing pattern of beat and tempo of the playing music by the proposed hand gesture recognition using either CFP tracking or motion histogram matching. The experimental results on the test data set show that the musical time pattern and tempo recognition rate is over 86.42% for the motion histogram matching, and 79.75% for the CFP tracking.

SLAM by Combining Multidimensional Scaling and Particle Filtering

This paper presents an algorithm for the simultaneous localization and mapping (SLAM) problem. Inspired by the basic idea of the FastSLAM which separates the robot pose estimation problem and mapping problem, we use the particle filter (PF) to estimate the pose of individual robot and use the multidimensional scaling (MDS), one of the distance mapping method, to find the relative coordinates of landmarks toward the robot. We apply the proposed algorithm to not only the single robot SLAM, but also the multi-robot SLAM. Experimental results demonstrate the effectiveness of the proposed algorithm over the FastSLAM.