• The Journal of the Korea Contents Association
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• v.16 no.5
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• pp.104-112
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• 2016

The Adaboost chooses a good set of features in rounds. On each round, it chooses the optimal feature and its threshold value by minimizing the weighted error of classification. The involved process of classification performs a hard decision. In this paper, we expand the process of classification to a soft fuzzy decision. We believe this expansion could allow some flexibility to the Adaboost algorithm as well as a good performance especially when the size of a training data set is not large enough. The typical Adaboost algorithm assigns a same weight to each training datum on the first round of a training process. We propose a new algorithm to assign different initial weights based on some statistical properties of involved features. In experimental results, we assess that the proposed method shows higher performance than the traditional one.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.22-28
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• 2010

Adaboost is well known for a representative learner as one of the kernel methods. Adaboost which is based on the statistical learning theory shows good generalization performance and has been applied to various pattern recognition problems. However, Adaboost is basically to deal with a two-class classification problem, so we cannot solve directly a multi-class problem with Adaboost. One-Vs-All and Pair-Wise have been applied to solve the multi-class classification problem, which is one of the multi-class problems. The two methods above are ones of the output coding methods, a general approach for solving multi-class problem with multiple binary classifiers, which decomposes a complex multi-class problem into a set of binary problems and then reconstructs the outputs of binary classifiers for each binary problem. However, two methods cannot show good performance. In this paper, we propose the method to solve a multi-target classification problem by using radial basis function of Adaboost weak classifier.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.806-813
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• 2010

Human facial expression shows human's emotion most exactly, so it can be used as the most efficient tool for delivering human's intention to computer. For fast and exact recognition of human's facial expression on a 2D image, this paper proposes a new method which integrates an Discrete Adaboost classification algorithm and a neural network based recognition algorithm. In the first step, Adaboost algorithm finds the position and size of a face in the input image. Second, input detected face image into 5 Adaboost strong classifiers which have been trained for each facial expressions. Finally, neural network based recognition algorithm which has been trained with the outputs of Adaboost strong classifiers determines final facial expression result. The proposed algorithm guarantees the realtime and enhanced accuracy by utilizing fastness and accuracy of Adaboost classification algorithm and reliability of neural network based recognition algorithm. In this paper, the proposed algorithm recognizes five facial expressions such as neutral, happiness, sadness, anger and surprise and achieves 86~95% of accuracy depending on the expression types in real time.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.761-766
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• 2007

이 논문에서는 커널 Edge Map 방식의 얼굴의 특징점을 검출하는 방법과 Adaboost를 이용한 얼굴의 특징점을 검출하는 방법을 이용하여 좀 더 강인한 얼굴의 특징점을 검출해 낸다. 커널 Edge Map을 이용한 방법은 기존의 10개의 커널을 이용하여 검출된 Edge를 이용하지 않고 좀 더 빠르게 검출해내기 위해 2개의 커널을 이용하여 얼굴의 특징점을 검출해 낸다. 이렇게 만들어진 얼굴의 특징점 후보군들에서 Adaboost를 이용하여 좀 더 정확하고 빠른 특징점을 찾을 수 있게 된다. Adaboost를 이용한 방법은 각각의 특징점들을 오프라인 상에서 학습을 하고 실시간으로 특징점을 검출하는 방법을 사용하였다. Edge를 이용한 방법으로 이미지의 전처리를 하여 후보군을 찾고 그 후보군과 Adaboost를 이용한 후보군들의 조합으로 인해 좀 더 강인하게 얼굴의 특징점을 찾을 수 있다.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.3
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• pp.149-154
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• 2017

The MCT-Adaboost algorithm chooses an optimal set of features in each rounds. On each round, it chooses the best feature by calculate minimizing error rate using feature index and MCT kernel distribution. The involved process of weak classification executed by a hard decision. This decision occurs some problems when it chooses ambiguous kernel feature. In this paper, we propose the modified MCT-Adaboost classification using soft decision. The typical MCT-Adaboost assigns a same initial weights to each datum. This is because, they assume that all information of database is blind. We assign different initial weights with our propose new algorithm using some statistical properties of involved features. In experimental results, we confirm that our method shows better performance than the traditional one.

• Journal of the Korea Institute of Building Construction
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• v.11 no.5
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• pp.523-529
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• 2011

In a tall building construction with reinforced concrete structures, the selection of an appropriate formwork system is a crucial factor for the success of the project. Thus, selecting an appropriate formwork system affects the entire construction duration and cost, as well as subsequent construction activities. However, in practice, the selection of an appropriate formwork system has depended mainly on the intuitive and subjective opinion of working level employees with restricted experience. Therefore, in this study, a formwork system selection model using the Adaboost algorithm is proposed to support the selection of a formwork system that is suitable for the construction site conditions. To validate the applicability of the proposed model, the selection models Adaboost and ANN were both applied to actual case data of tall building construction in Korea. The Adaboost model showed slightly better accuracy than that of the ANN model. The Adaboost model can assist engineers to determine the appropriate formwork system at the inception of future projects.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.1-11
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• 2008

In this paper, we propose the new eye detection method by using adaboost (adaptive boosting) method. Also, to reduce the false alarm rate which identifies the non-eye region as genuine eye that is the Problems of previous method using conventional adaboost, we proposed the post processing methods which used the cornea specular reflection and determined the optimized ratio of eye detecting box. Based on detected eye region by using adaboost, we performed the double circular edge detector for localizing a pupil and an iris region at the same time. Experimental results showed that the accuracy of eye detection was about 98% and the processing time was less than 1 second in mobile device.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1978-1984
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• 2016

This paper proposes a robust vehicle detecting method by using Adaboost and CLAHE(Contrast-Limit Adaptive Histogram Equalization). We propose two method to detect vehicle effectively. First, we are able to judge rainy and night by converting RGB value to brightness. Second, we can detect a taillight, designate a ROI(Region Of Interest) by using CLAHE. And then, we choose an Adaboost algorithm by comparing traditional vehicle detecting method such as GMM(Gaussian Mixture Model), Optical flow and Adaboost. In this paper, we use proposed method and get better performance of detecting vehicle. The precision and recall score of proposed method are 0.85 and 0.87. That scores are better than GMM and optical flow.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.8
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• pp.1129-1135
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• 2013

A new vehicle detection algorithm based on the multiple optimal Adaboost classifiers with optimal feature selection is proposed. It consists of two major modules: 1) Theoretical DDISF(Distance Dependent Image Scaling Factor) based image scaling by site modeling of the installed cameras. and 2) optimal features selection by Haar-like feature analysis depending on the distance of the vehicles. The experimental results of the proposed algorithm shows improved recognition rate compare to the previous methods for vehicles and non-vehicles. The proposed algorithm shows about 96.43% detection rate and about 3.77% false alarm rate. These are 3.69% and 1.28% improvement compared to the standard Adaboost algorithmt.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.4
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• pp.42-53
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• 2012

In pattern recognition and machine learning society, classification has been a classical problem and the most widely researched area. Adaptive boosting also known as Adaboost has been successfully applied to binary classification problems. It is a kind of boosting algorithm capable of constructing a strong classifier through a weighted combination of weak classifiers. On the other hand, the PCA and LDA algorithms are the most popular linear feature extraction methods used mainly for dimensionality reduction. In this paper, the combination of Adaboost and feature extraction methods is proposed for efficient classification of two class data. Conventionally, in classification problems, the roles of feature extraction and classification have been distinct, i.e., a feature extraction method and a classifier are applied sequentially to classify input variable into several categories. In this paper, these two steps are combined into one resulting in a good classification performance. More specifically, each projection vector is treated as a weak classifier in Adaboost algorithm to constitute a strong classifier for binary classification problems. The proposed algorithm is applied to UCI dataset and FRGC dataset and showed better recognition rates than sequential application of feature extraction and classification methods.