• Title/Summary/Keyword: Hidden Markov Model

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A Soccer Video Analysis Using Product Hierarchical Hidden Markov Model (PHHMM(Product Hierarchical Hidden Markov Model)을 이용한 축구 비디오 분석)

  • Kim, Moo-Sung;Kang, Hang-Bong
    • Proceedings of the IEEK Conference
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    • 2006.06a
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    • pp.681-682
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    • 2006
  • 일반적으로 축구 비디오 데이터는 멀티모달과 멀티레이어 속성을 지닌다. 이러한 데이터를 다루기 적합한 모델은 동적 베이지안 네트워크(Dynamic Bayesian Network: DBN) 형태의 위계적 은닉 마르코프 모델(Hierarchical Hidden Markov Model: HHMM)이다. 이러한 HHMM 중 다중속성의 특징들이 서로 상호작용하는 PHHMM(Product Hierarchical Hidden Markov Model)이 있다. 본 논문에서는 PHHMM 을 축구 경기의 Play/Break 이벤트 검색 및 분석에 적용하였고 바람직한 결과를 얻었다.

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An Automatic Summarization of Call-For-Paper Documents Using a 2-Phase hidden Markov Model (2단계 은닉 마코프 모델을 이용한 논문 모집 공고의 자동 요약)

  • Kim, Jeong-Hyun;Park, Seong-Bae;Lee, Sang-Jo;Park, Se-Young
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.2
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    • pp.243-250
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    • 2008
  • This paper proposes a system which extracts necessary information from call-for-paper (CFP) documents using a hidden Markov model (HMM). Even though a CFP does not follow a strict form, there is, in general, a relatively-fixed sequence of information within most CFPs. Therefore, a hiden Markov model is adopted to analyze CFPs which has an advantage of processing consecutive data. However, when CFPs are intuitively modeled with a hidden Markov model, a problem arises that the boundaries of the information are not recognized accurately. In order to solve this problem, this paper proposes a two-phrase hidden Markov model. In the first step, the P-HMM (Phrase hidden Markov model) which models a document with phrases recognizes CFP documents locally. Then, the D-HMM (Document hidden Markov model) grasps the overall structure and information flow of the document. The experiments over 400 CFP documents grathered on Web result in 0.49 of F-score. This performance implies 0.15 of F-measure improvement over the HMM which is intuitively modeled.

Video Summarization Using Hidden Markov Model (은닉 마르코브 모델을 이용한 비디오 요약 시스템)

  • 박호식;배철수
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.8 no.6
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    • pp.1175-1181
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    • 2004
  • This paper proposes a system to analyze and summarize the video shots of baseball game TV program into fifteen categories. Our System consists of three modules: feature extraction, Hidden Markov Model (HMM) training, and video shot categorization. Video Shots belongs to the same class are not necessarily similar, so we require that the training set is large enough to include video shot with all possible variations to create a robust Hidden Markov Model. In the experiments, we have illustrated that our system can recognize the 15 different shot classes with a success ratio of 84.72%.

Non-Cooperative Game Joint Hidden Markov Model for Spectrum Allocation in Cognitive Radio Networks

  • Jiao, Yan
    • International journal of advanced smart convergence
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    • v.7 no.1
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    • pp.15-23
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    • 2018
  • Spectrum allocation is a key operation in cognitive radio networks (CRNs), where secondary users (SUs) are usually selfish - to achieve itself utility maximization. In view of this context, much prior lit literature proposed spectrum allocation base on non-cooperative game models. However, the most of them proposed non-cooperative game models based on complete information of CRNs. In practical, primary users (PUs) in a dynamic wireless environment with noise uncertainty, shadowing, and fading is difficult to attain a complete information about them. In this paper, we propose a non-cooperative game joint hidden markov model scheme for spectrum allocation in CRNs. Firstly, we propose a new hidden markov model for SUs to predict the sensing results of competitors. Then, we introduce the proposed hidden markov model into the non-cooperative game. That is, it predicts the sensing results of competitors before the non-cooperative game. The simulation results show that the proposed scheme improves the energy efficiency of networks and utilization of SUs.

A Probabilistic Tracking Mechanism for Luxury Purchase Implemented by Hidden Markov Model, Bayesian Inference, Customer Satisfaction and Net Promoter Score (고객만족, NPS, Bayesian Inference 및 Hidden Markov Model로 구현하는 명품구매에 관한 확률적 추적 메카니즘)

  • Hwang, Sun Ju;Rhee, Jung Soo
    • Journal of the Korea Industrial Information Systems Research
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    • v.23 no.6
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    • pp.79-94
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    • 2018
  • The purpose of this study is to specify a probabilistic tracking mechanism for customer luxury purchase implemented by hidden Markov model, Bayesian inference, customer satisfaction and net promoter score. In this paper, we have designed a probabilistic model based on customer's actual data containing purchase or non-purchase states by tracking the SPC chain : customer satisfaction -> customer referral -> purchase/non-purchase. By applying hidden Markov model and Viterbi algorithm to marketing theory, we have developed the statistical model related to probability theories and have found the best purchase pattern scenario from customer's purchase records.

Hand Gesture Recognition Using HMM(Hidden Markov Model) (HMM(Hidden Markov Model)을 이용한 핸드 제스처인식)

  • Ha, Jeong-Yo;Lee, Min-Ho;Choi, Hyung-Il
    • Journal of Digital Contents Society
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    • v.10 no.2
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    • pp.291-298
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    • 2009
  • In this paper we proposed a vision based realtime hand gesture recognition method. To extract skin color, we translate RGB color space into YCbCr color space and use CbCr color for the final extraction. To find the center of extracted hand region we apply practical center point extraction algorithm. We use Kalman filter to tracking hand region and use HMM(Hidden Markov Model) algorithm (learning 6 type of hand gesture image) to recognize it. We demonstrated the effectiveness of our algorithm by some experiments.

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Condition Monitoring of Rotating Machine with a Change in Speed Using Hidden Markov Model (은닉 마르코프 모델을 이용한 속도 변화가 있는 회전 기계의 상태 진단 기법)

  • Jang, M.;Lee, J.M.;Hwang, Y.;Cho, Y.J.;Song, J.B.
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.22 no.5
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    • pp.413-421
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    • 2012
  • In industry, various rotating machinery such as pumps, gas turbines, compressors, electric motors, generators are being used as an important facility. Due to the industrial development, they make high performance(high-speed, high-pressure). As a result, we need more intelligent and reliable machine condition diagnosis techniques. Diagnosis technique using hidden Markov-model is proposed for an accurate and predictable condition diagnosis of various rotating machines and also has overcame the speed limitation of time/frequency method by using compensation of the rotational speed of rotor. In addition, existing artificial intelligence method needs defect state data for fault detection. hidden Markov model can overcome this limitation by using normal state data alone to detect fault of rotational machinery. Vibration analysis of step-up gearbox for wind turbine was applied to the study to ensure the robustness of diagnostic performance about compensation of the rotational speed. To assure the performance of normal state alone method, hidden Markov model was applied to experimental torque measuring gearbox in this study.

The Method for Face Recognition using Wavelet Coefficients and Hidden Markov Model (웨이블렛 계수와 Hidden Markov Model를 이용한 얼굴인식 기법)

  • 이경아;이대종;박장환;전명근
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2003.09b
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    • pp.162-165
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    • 2003
  • 본 논문에서는 웨이블렛 계수와 Hidden Markov Model(HMM)이용한 얼굴인식 알고리즘을 제안한다. 입력 영상은 이산웨이블렛을 기반으로 한 다해상도 분석기법을 사용하여 데이터 수를 압축한 후, 각각의 해상도에서 얻어진 웨이블렛 계수를 특징벡터로 사용하여 HMM의 모델을 생성한다. 인식단계 에서는 웨이블렛 변환에 의해 생성된 개별대역의 인식값을 더하여 상호 보완함으로써 인식률을 높일 수 있었다. 제안된 알고리즘의 타당성을 검증하기 위하여 기본적 알고리즘인 벡터 양자화(VQ) 기법을 적용한 경우와 기존 얼굴인식에 제안된 DCT-HMM을 이용한 기법과의 인식률 비교를 한 결과, 제안된 방법이 우수한 성능을 보임을 알 수 있었다.

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On-Line Character Recognition using Hidden Markov Model and Genetic Algorithm (Hidden Markov Model 과 Genetic Algorithm을 이용한 온라인 문자인식에 관한 연구)

  • 홍영표;장춘서
    • Proceedings of the IEEK Conference
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    • 2000.11c
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    • pp.29-32
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    • 2000
  • HMM(Hidden Markov Model)은 시간적인 정보를 토대로 하는 수학적인 방법으로서 문자인식에 많이 사용되어지고 있다. 그런데 HMM이 적용되고자 하는 문제에서 사용되어지는 상태 수와 HMM에서 사용되어지는 parameter들은 처음에 결정되는 값들에 의해서 상당히 많은 영향을 받게 된다. 따라서 한글의 특성을 이용한 HMM의 상태 수를 결정한 후 결정되어진 각각의 HMM parameter들을 Genetic Algorithm을 이용하였다. Genetic Algorithm은 매개변수 최적화 문제에 대하여 자연의 진화 원리를 마땅한 알고리즘으로 선택, 교배, 돌연변이 연산을 이용하여 최적의 개체를 구하게 된다. 여기서는 HMM에서의 Viterbi Algorithm을 적합도 검사에 사용하였다.

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Multiple State Hidden Markov Model to Predict Transmembrane Protein Topology

  • Chi, Sang-Mun
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.4
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    • pp.1019-1031
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    • 2004
  • This paper describes a new modeling method for the prediction of transmembrane protein topology. The structural regions of the transmembrane protein have been modeled by means of a multiple state hidden Markov model that has provided for the detailed modeling of the heterogeneous amino acid distributions of each structural region. Grammatical constraints have been incorporated to the prediction method in order to capture the biological order of membrane protein topology. The proposed method correctly predicted 76% of all membrane spanning regions and 92% sidedness of the integration when all membrane spanning regions were found correctly.

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