• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.504-509
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• 2008

Support Vector Machine (SVM) is one of powerful learning machine and has been applied to varying task with generally acceptable performance. The success of SVM for classification tasks in one domain is affected by features which represent the instance of specific class. Given the representative and discriminative features, SVM learning will give good generalization and consequently we can obtain good classifier. In this paper, we will assess the problem of feature choices for human detection tasks and measure the performance of each feature. Here we will consider HOG-family feature. As a natural extension of SVM, we combine SVM with Principal Component Analysis (PCA) to reduce dimension of features while retaining most of discriminative feature vectors.

• The Journal of Korea Robotics Society
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• v.12 no.2
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• pp.217-227
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• 2017

Crosswalk detection is an important part of the Pedestrian Protection System in autonomous vehicles. Different methods of crosswalk detection have been introduced so far using crosswalk edge features, the distance between crosswalk blocks, laser scanning, Hough Transformation, and Fourier Transformation. However, most of these methods failed to detect crosswalks accurately, when they are damaged, faded away or partly occluded. Furthermore, these methods face difficulties when applying on real road environment where there are lot of vehicles. In this paper, we solve this problem by first using a region based binarization technique and x-axis histogram to detect the candidate crosswalk areas. Then, we apply Support Vector Machine (SVM) based classification method to decide whether the candidate areas contain a crosswalk or not. Experiment results prove that our method can detect crosswalks in different environment conditions with higher recognition rate even they are faded away or partly occluded.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.971-972
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• 2008

In this paper, we present a novel method for classifying pornography images using adaptive skin detection. From an input image, we detect initial skin regions and construct an adaptive skin probability density model using color information for the detected skin regions. From the skin probability density model, we extract feature vectors and train the images using Support Vector Machine to classify pornography images.

• ETRI Journal
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• v.29 no.4
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• pp.507-515
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• 2007

Skeleton transform of which the medial axis transform is the most popular has been proposed as a useful shape abstraction tool for the representation and modeling of human posture. This paper explains this proposition with a description of the areas in which skeletons could serve to enable the representation of shapes. We present algorithms for two-dimensional posture modeling using the developed simplified shock graph (SSG). The efficacy of SSG extracted feature vectors as shape descriptors are also evaluated using three different classifiers, namely, decision tree, multilayer perceptron, and support vector machine. The paper concludes with a discussion of the issues involved in using shock graphs to model and classify human postures.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2112-2115
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• 2003

The system for the real time face detection is described in this paper. For face verification, support vector machine (SVM) was utilized. Although SVM performs quit well, SVM has a drawback that the computational cost is high because all pixels in a mask are used as an input feature vector of SVM. To resolve this drawback, a method to reduce the dimension of feature vectors using the integer DCT was proposed. Also for the real time face detection applications, low-complexity methods for face candidate detection in a gray image were used. As a result, the accurate face detection was performed in real time.

• Industrial Engineering and Management Systems
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• v.14 no.3
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• pp.318-324
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• 2015

This paper proposes a dynamic short-term load forecasting method that utilizes a new sequential learning algorithm based on Relevance Vector Machine (RVM). The method performs general optimization of weights and hyperparameters using the current relevance vectors and newly arriving data. By doing so, the proposed algorithm is trained with the most recent data. Consequently, it extends the RVM algorithm to real-time and nonstationary learning processes. The results of application of the proposed algorithm to prediction of electrical loads indicate that its accuracy is comparable to that of existing nonparametric learning algorithms. Further, the proposed model reduces computational complexity.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.225-232
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• 2003

The support vector machine (SVM) is becoming increasingly popular in classification. The import vector machine (IVM) has been introduced for its advantages over SMV. This paper tries to improve the IVM. The proposed method, which is referred to as the polychotomous machine (PM), uses the Newton-Raphson method to find estimates of coefficients, and the Rao and Wald tests, respectively, for addition and deletion of import points. Because the PM basically follows the same addition step and adopts the deletion step, it uses, typically, less import vectors than the IVM without loosing accuracy. Simulated and real data sets are used to illustrate the performance of the proposed method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.6
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• pp.581-590
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• 2014

With rapid land development, land category should be updated on a regular basis. However, manual field surveys have certain limitations. In this study, attempts were made to extract a feature vector considering spectral signature by parcel, PIMP (Percent Imperviousness), texture, and VIs (Vegetation Indices) based on RapidEye satellite image and cadastral map. A total of nine land categories in which feature vectors were significantly extracted from the images were selected and classified using SVM (Support Vector Machine). According to accuracy assessment, by comparing the cadastral map and classification result, the overall accuracy was 0.74. In the paddy-field category, in particular, PO acc. (producer's accuracy) and US acc. (user's accuracy) were highest at 0.85 and 0.86, respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.153-156
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• 2007

In this paper, we propose a fast face detection approach using PCA and SVM. In our detection system, first we filter the face potential area using statistical feature which is generated by analyzing local histogram distribution. And then, we use SVM classifier to detect whether there are faces present in the test image. Support Vector Machine (SVM) has great performance in classification task. PCA is used for dimension reduction of sample data. After PCA transform, the feature vectors, which are used for training SVM classifier, are generated. Our tests in this paper are based on CMU face database.

• Journal of Information Processing Systems
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• v.10 no.4
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• pp.555-567
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• 2014

Biometric performance improvement is a challenging task. In this paper, a hierarchical strategy fusion based on multimodal biometric system is presented. This strategy relies on a combination of several biometric traits using a multi-level biometric fusion hierarchy. The multi-level biometric fusion includes a pre-classification fusion with optimal feature selection and a post-classification fusion that is based on the similarity of the maximum of matching scores. The proposed solution enhances biometric recognition performances based on suitable feature selection and reduction, such as principal component analysis (PCA) and linear discriminant analysis (LDA), as much as not all of the feature vectors components support the performance improvement degree.