• Title/Summary/Keyword: classifier

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Multiple-Classifier Combination based on Image Degradation Model for Low-Quality Image Recognition (저화질 영상 인식을 위한 화질 저하 모델 기반 다중 인식기 결합)

  • Ryu, Sang-Jin;Kim, In-Jung
    • The KIPS Transactions:PartB
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    • v.17B no.3
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    • pp.233-238
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    • 2010
  • In this paper, we propose a multiple classifier combination method based on image degradation modeling to improve recognition performance on low-quality images. Using an image degradation model, it generates a set of classifiers each of which is specialized for a specific image quality. In recognition, it combines the results of the recognizers by weighted averaging to decide the final result. At this time, the weight of each recognizer is dynamically decided from the estimated quality of the input image. It assigns large weight to the recognizer specialized to the estimated quality of the input image, but small weight to other recognizers. As the result, it can effectively adapt to image quality variation. Moreover, being a multiple-classifier system, it shows more reliable performance then the single-classifier system on low-quality images. In the experiment, the proposed multiple-classifier combination method achieved higher recognition rate than multiple-classifier combination systems not considering the image quality or single classifier systems considering the image quality.

A Study on Modulation Classification of PSK Signals Based on Statistical Moments (통계적 모먼트에 의한 PSK 신호의 변조분류에 관한 연구)

  • 이원철;한영열
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.19 no.6
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    • pp.1004-1015
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    • 1994
  • Modulation type classifier based on statistical moments has been successfully employed to classify PSK signals. Previously, the classifier developed utilizes the statistical moment of samples of the received signal phase, which may be difficult to extract from received signal. In this paper we propose a new moments-based classifier to classify PSK signals by using the moments of the demodulated signal for PSK. THe demodulated signal can be easily extracted from the conventional demodulation of PSK. The evaluation of the performance of the proposed classifier for PSK signals has been investigated in additive white Gaussian noise environment using the exact distribution of the demodulated signal. The performances of classifier in terms of probability of misclassification were evaluated. We found that the coherent system classifier gave 4dB improvement for BPSK and 3dB for QPSK over noncoherent system classifier, when the probability of misclassification is 10 and m equals to 4.

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Fast Color Classifier Using Neural Networks in RGB and YUV Color-Space

  • Lee, Seonghoon;Lee, Minjung;Park, Youngkiu
    • 제어로봇시스템학회:학술대회논문집
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    • pp.109.3-109
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    • 2002
  • 1. Introduction 2. Vision system 3. Effect of brightness variations 4. Color classifier using multi-layer neural network 5. Experimental result of color classifier 6. Applications for robot soccer system 7. Conclusion

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Development of Adaptive AE Signal Pattern Recognition Program and Application to Classification of Defects in Metal Contact Regions of Rotating Component (적응형 AE신호 형상 인식 프로그램 개발자 회전체 금속 접촉부 이상 분류에 관한 적용 연구)

  • Lee, K.Y.;Lee, C.M.;Kim, J.S.
    • Journal of the Korean Society for Nondestructive Testing
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    • v.15 no.4
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    • pp.520-530
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    • 1996
  • In this study, the artificial defects in rotary compressor are classified using pattern recognition of acoustic emission signal. For this purpose the computer program is developed. The neural network classifier is compared with the statistical classifier such as the linear discriminant function classifier and empirical Bayesian classifier. It is concluded that the former is better. It is possible to acquire the recognition rate of above 99% by neural network classifier.

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Behavior strategies of Soccer Robot using Classifier System (분류자 시스템을 이용한 축구 로봇의 행동 전략)

  • Sim, Kwee-Bo;Kim, Ji-Youn
    • Journal of the Korean Institute of Intelligent Systems
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    • v.12 no.4
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    • pp.289-293
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    • 2002
  • Learning Classifier System (LCS) finds a new rule set using genetic algorithm (GA). In this paper, The Zeroth Level Classifier System (ZCS) is applied to evolving the strategy of a robot soccer simulation game (SimuroSot), which is a state varying dynamical system changed over time, as GBML (Genetic Based Machine Learning) and we show the effectiveness of the proposed scheme through the simulation of robot soccer.

Generation of Pattern Classifiers Based on Linear Nongroup CA

  • Choi, Un-Sook;Cho, Sung-Jin;Kim, Han-Doo
    • Journal of Korea Multimedia Society
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    • v.18 no.11
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    • pp.1281-1288
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    • 2015
  • Nongroup Cellular Automata(CA) having two trees in the state transition diagram of a CA is suitable for pattern classifier which divides pattern set into two classes. Maji et al. [1] classified patterns by using multiple attractor cellular automata as a pattern classifier with dependency vector. In this paper we propose a method of generation of a pattern classifier using feature vector which is the extension of dependency vector. In addition, we propose methods for finding nonreachable states in the 0-tree of the state transition diagram of TPMACA corresponding to the given feature vector for the analysis of the state transition behavior of the generated pattern classifier.

A Neuro-Fuzzy Model Approach for the Land Cover Classification

  • Han, Jong-Gyu;Chi, Kwang-Hoon;Suh, Jae-Young
    • Proceedings of the KSRS Conference
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    • pp.122-127
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    • 1998
  • This paper presents the neuro-fuzzy classifier derived from the generic model of a 3-layer fuzzy perceptron and developed the classification software based on the neuro-fuzzl model. Also, a comparison of the neuro-fuzzy and maximum-likelihood classifiers is presented in this paper. The Airborne Multispectral Scanner(AMS) imagery of Tae-Duk Science Complex Town were used for this comparison. The neuro-fuzzy classifier was more considerably accurate in the mixed composition area like "bare soil" , "dried grass" and "coniferous tree", however, the "cement road" and "asphalt road" classified more correctly with the maximum-likelihood classifier than the neuro-fuzzy classifier. Thus, the neuro-fuzzy model can be used to classify the mixed composition area like the natural environment of korea peninsula. From this research we conclude that the neuro-fuzzy classifier was superior in suppression of mixed pixel classification errors, and more robust to training site heterogeneity and the use of class labels for land use that are mixtures of land cover signatures.

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A Novel Posterior Probability Estimation Method for Multi-label Naive Bayes Classification

  • Kim, Hae-Cheon;Lee, Jaesung
    • Journal of the Korea Society of Computer and Information
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    • v.23 no.6
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    • pp.1-7
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    • 2018
  • A multi-label classification is to find multiple labels associated with the input pattern. Multi-label classification can be achieved by extending conventional single-label classification. Common extension techniques are known as Binary relevance, Label powerset, and Classifier chains. However, most of the extended multi-label naive bayes classifier has not been able to accurately estimate posterior probabilities because it does not reflect the label dependency. And the remaining extended multi-label naive bayes classifier has a problem that it is unstable to estimate posterior probability according to the label selection order. To estimate posterior probability well, we propose a new posterior probability estimation method that reflects the probability between all labels and labels efficiently. The proposed method reflects the correlation between labels. And we have confirmed through experiments that the extended multi-label naive bayes classifier using the proposed method has higher accuracy then the existing multi-label naive bayes classifiers.

Optimal Classifier Ensemble Design for Vehicle Detection Using GAVaPS (자동차 검출을 위한 GAVaPS를 이용한 최적 분류기 앙상블 설계)

  • Lee, Hee-Sung;Lee, Jae-Hung;Kim, Eun-Tai
    • Journal of Institute of Control, Robotics and Systems
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    • v.16 no.1
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    • pp.96-100
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    • 2010
  • This paper proposes novel genetic design of optimal classifier ensemble for vehicle detection using Genetic Algorithm with Varying Population Size (GAVaPS). Recently, many classifiers are used in classifier ensemble to deal with tremendous amounts of data. However the problem has a exponential large search space due to the increasing the number of classifier pool. To solve this problem, we employ the GAVaPS which outperforms comparison with simple genetic algorithm (SGA). Experiments are performed to demonstrate the efficiency of the proposed method.

Learning Rules for AMR of Collision Avoidance using Fuzzy Classifier System (퍼지 분류자 시스템을 이용한 자율이동로봇의 충돌 회피 학습)

  • 반창봉;전효병;심귀보
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.179-182
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    • 2000
  • A Classifier System processes a discrete coded information from the environment. When the system codes the information to discontinuous data, it loses excessively the information of the environment. The Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. It is the FCS that applies this ability of the machine learning to the concept of fuzzy controller. It is that the antecedent and consequent of classifier is same as a fuzzy rule of the rule base. In this paper, the FCS is the Michigan style and fuzzifies the input values to create the messages. The system stores those messages in the message list and uses the implicit Bucket Brigade Algorithms. Also the FCS employs the Genetic Algorithms(GAs) to make new rules and modify rules when performance of the system needs to be improved. We will verify the effectiveness of the proposed FCS by applying it to AMR avoiding the obstacle.

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