• Title, Summary, Keyword: Gaussian mixture model-based

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Extraction of Infrared Target based on Gaussian Mixture Model

  • Shin, Do Kyung;Moon, Young Shik
    • IEIE Transactions on Smart Processing and Computing
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    • v.2 no.6
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    • pp.332-338
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    • 2013
  • We propose a method for target detection in Infrared images. In order to effectively detect a target region from an image with noises and clutters, spatial information of the target is first considered by analyzing pixel distributions of projections in horizontal and vertical directions. These distributions are represented as Gaussian distributions, and Gaussian Mixture Model is created from these distributions in order to find thresholding points of the target region. Through analyzing the calculated Gaussian Mixture Model, the target region is detected by eliminating various backgrounds such as noises and clutters. This is performed by using a novel thresholding method which can effectively detect the target region. As experimental results, the proposed method has achieved better performance than existing methods.

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Gaussian Mixture Model Based Smoke Detection Algorithm Robust to Lights Variations (Gaussian 혼합모델 기반 조명 변화에 강건한 연기검출 알고리즘)

  • Park, Jang-Sik;Song, Jong-Kwan;Yoon, Byung-Woo
    • The Journal of the Korea institute of electronic communication sciences
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    • v.7 no.4
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    • pp.733-739
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    • 2012
  • In this paper, a smoke detection algorithm robust to brightness and color variations depending on time and weather is proposed. The proposed smoke detection algorithm specifies the candidate region using difference images of input and background images, determines smoke by comparing feature coefficients of Gaussian mixture model of difference images. Thresholds for specifying candidate region is divided by four levels according to average brightness and chrominance of input images. Clusters of Gaussian mixture models of difference images are aligned according to average brightness. Smoke is determined by comparing distance of Gaussian mixture model parameters. The proposed algorithm is implemented by media dedicated DSP. As results of experiments, it is shown that the proposed algorithm is effective to detect smoke with camera installed outdoor.

Online nonparametric Bayesian analysis of parsimonious Gaussian mixture models and scenes clustering

  • Zhou, Ri-Gui;Wang, Wei
    • ETRI Journal
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    • v.43 no.1
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    • pp.74-81
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    • 2021
  • The mixture model is a very powerful and flexible tool in clustering analysis. Based on the Dirichlet process and parsimonious Gaussian distribution, we propose a new nonparametric mixture framework for solving challenging clustering problems. Meanwhile, the inference of the model depends on the efficient online variational Bayesian approach, which enhances the information exchange between the whole and the part to a certain extent and applies to scalable datasets. The experiments on the scene database indicate that the novel clustering framework, when combined with a convolutional neural network for feature extraction, has meaningful advantages over other models.

Optimization of Gaussian Mixture in CDHMM Training for Improved Speech Recognition

  • Lee, Seo-Gu;Kim, Sung-Gil;Kang, Sun-Mee;Ko, Han-Seok
    • Speech Sciences
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    • v.5 no.1
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    • pp.7-21
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    • 1999
  • This paper proposes an improved training procedure in speech recognition based on the continuous density of the Hidden Markov Model (CDHMM). Of the three parameters (initial state distribution probability, state transition probability, output probability density function (p.d.f.) of state) governing the CDHMM model, we focus on the third parameter and propose an efficient algorithm that determines the p.d.f. of each state. It is known that the resulting CDHMM model converges to a local maximum point of parameter estimation via the iterative Expectation Maximization procedure. Specifically, we propose two independent algorithms that can be embedded in the segmental K -means training procedure by replacing relevant key steps; the adaptation of the number of mixture Gaussian p.d.f. and the initialization using the CDHMM parameters previously estimated. The proposed adaptation algorithm searches for the optimal number of mixture Gaussian humps to ensure that the p.d.f. is consistently re-estimated, enabling the model to converge toward the global maximum point. By applying an appropriate threshold value, which measures the amount of collective changes of weighted variances, the optimized number of mixture Gaussian branch is determined. The initialization algorithm essentially exploits the CDHMM parameters previously estimated and uses them as the basis for the current initial segmentation subroutine. It captures the trend of previous training history whereas the uniform segmentation decimates it. The recognition performance of the proposed adaptation procedures along with the suggested initialization is verified to be always better than that of existing training procedure using fixed number of mixture Gaussian p.d.f.

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A Density-based Clustering Method

  • Ahn, Sung Mahn;Baik, Sung Wook
    • Communications for Statistical Applications and Methods
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    • v.9 no.3
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    • pp.715-723
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    • 2002
  • This paper is to show a clustering application of a density estimation method that utilizes the Gaussian mixture model. We define "closeness measure" as a clustering criterion to see how close given two Gaussian components are. Closeness measure is defined as the ratio of log likelihood between two Gaussian components. According to simulations using artificial data, the clustering algorithm turned out to be very powerful in that it can correctly determine clusters in complex situations, and very flexible in that it can produce different sizes of clusters based on different threshold valuesold values

(Lip Recognition Using Active Shape Model and Gaussian Mixture Model) (Active Shape 모델과 Gaussian Mixture 모델을 이용한 입술 인식)

  • 장경식;이임건
    • Journal of KIISE:Software and Applications
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    • v.30 no.5_6
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    • pp.454-460
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    • 2003
  • In this paper, we propose an efficient method for recognizing human lips. Based on Point Distribution Model, a lip shape is represented as a set of points. We calculate a lip model and the distribution of shape parameters using Principle Component Analysis and Gaussian mixture, respectively. The Expectation Maximization algorithm is used to determine the maximum likelihood parameter of Gaussian mixture. The lip contour model is derived by using the gray value changes at each point and in regions around the point and used to search the lip shape in a image. The experiments have been performed for many images, and show very encouraging result.

Lip Shape Representation and Lip Boundary Detection Using Mixture Model of Shape (형태계수의 Mixture Model을 이용한 입술 형태 표현과 입술 경계선 추출)

  • Jang Kyung Shik;Lee Imgeun
    • Journal of Korea Multimedia Society
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    • v.7 no.11
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    • pp.1531-1539
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    • 2004
  • In this paper, we propose an efficient method for locating human lips. Based on Point Distribution Model and Principle Component Analysis, a lip shape model is built. Lip boundary model is represented based on the concatenated gray level distribution model. We calculate the distribution of shape parameters using Gaussian mixture. The problem to locate lip is simplified as the minimization problem of matching object function. The Down Hill Simplex Algorithm is used for the minimization with Gaussian Mixture for setting initial condition and refining estimate of lip shape parameter, which can refrain iteration from converging to local minima. The experiments have been performed for many images, and show very encouraging result.

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Model-based Clustering of DOA Data Using von Mises Mixture Model for Sound Source Localization

  • Dinh, Quang Nguyen;Lee, Chang-Hoon
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.13 no.1
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    • pp.59-66
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    • 2013
  • In this paper, we propose a probabilistic framework for model-based clustering of direction of arrival (DOA) data to obtain stable sound source localization (SSL) estimates. Model-based clustering has been shown capable of handling highly overlapped and noisy datasets, such as those involved in DOA detection. Although the Gaussian mixture model is commonly used for model-based clustering, we propose use of the von Mises mixture model as more befitting circular DOA data than a Gaussian distribution. The EM framework for the von Mises mixture model in a unit hyper sphere is degenerated for the 2D case and used as such in the proposed method. We also use a histogram of the dataset to initialize the number of clusters and the initial values of parameters, thereby saving calculation time and improving the efficiency. Experiments using simulated and real-world datasets demonstrate the performance of the proposed method.

A study on Gaussian mixture model deep neural network hybrid-based feature compensation for robust speech recognition in noisy environments (잡음 환경에 효과적인 음성 인식을 위한 Gaussian mixture model deep neural network 하이브리드 기반의 특징 보상)

  • Yoon, Ki-mu;Kim, Wooil
    • The Journal of the Acoustical Society of Korea
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    • v.37 no.6
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    • pp.506-511
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    • 2018
  • This paper proposes an GMM(Gaussian Mixture Model)-DNN(Deep Neural Network) hybrid-based feature compensation method for effective speech recognition in noisy environments. In the proposed algorithm, the posterior probability for the conventional GMM-based feature compensation method is calculated using DNN. The experimental results using the Aurora 2.0 framework and database demonstrate that the proposed GMM-DNN hybrid-based feature compensation method shows more effective in Known and Unknown noisy environments compared to the GMM-based method. In particular, the experiments of the Unknown environments show 9.13 % of relative improvement in the average of WER (Word Error Rate) and considerable improvements in lower SNR (Signal to Noise Ratio) conditions such as 0 and 5 dB SNR.