• Title/Summary/Keyword: Non Negative Factorization

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Vehicle Recognition using NMF in Urban Scene (도심 영상에서의 비음수행렬분해를 이용한 차량 인식)

  • Ban, Jae-Min;Lee, Byeong-Rae;Kang, Hyun-Chul
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.37 no.7C
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    • pp.554-564
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    • 2012
  • The vehicle recognition consists of two steps; the vehicle region detection step and the vehicle identification step based on the feature extracted from the detected region. Features using linear transformations have the effect of dimension reduction as well as represent statistical characteristics, and show the robustness in translation and rotation of objects. Among the linear transformations, the NMF(Non-negative Matrix Factorization) is one of part-based representation. Therefore, we can extract NMF features with sparsity and improve the vehicle recognition rate by the representation of local features of a car as a basis vector. In this paper, we propose a feature extraction using NMF suitable for the vehicle recognition, and verify the recognition rate with it. Also, we compared the vehicle recognition rate for the occluded area using the SNMF(sparse NMF) which has basis vectors with constraint and LVQ2 neural network. We showed that the feature through the proposed NMF is robust in the urban scene where occlusions are frequently occur.

Comparison of independent component analysis algorithms for low-frequency interference of passive line array sonars (수동 선배열 소나의 저주파 간섭 신호에 대한 독립성분분석 알고리즘 비교)

  • Kim, Juho;Ashraf, Hina;Lee, Chong-Hyun;Cheong, Myoung Jun
    • The Journal of the Acoustical Society of Korea
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    • v.38 no.2
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    • pp.177-183
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    • 2019
  • In this paper, we proposed an application method of ICA (Independent Component Analysis) to passive line array sonar to separate interferences from target signals in low frequency band and compared performance of three conventional ICA algorithms. Since the low frequency signals are received through larger bearing angles than other frequency bands, neighboring beam signals can be used to perform ICA as measurement signals of the ICA. We use three ICA algorithms such as Fast ICA, NNMF (Non-negative Matrix Factorization) and JADE (Joint Approximation Diagonalization of Eigen-matrices). Through experiments on real data obtained from passive line array sonar, it is verified that the interference can be separable from target signals by the suggested method and the JADE algorithm shows the best separation performance among the three algorithms.

Mono-To-Stereo Blind Upmix Using Non-Negative Matrix Factorization and Decorrelator (비음수 행렬 분해와 디코릴레이터를 이용한 모노-스테레오 블라인드 업믹스 기법)

  • Choi, Keun-Woo;Chon, Sang-Bae;Lee, Seok-Jin;Sung, Koeng-Mo
    • The Journal of the Acoustical Society of Korea
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    • v.29 no.8
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    • pp.509-515
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    • 2010
  • This paper presents a new method for upmixing mono signal to stereo signal with guaranteeing high stereophonic image quality (SIQ) and large apparent source width (ASW). The proposed method consists of analysis phase and synthesis phase. In analysis phase, a mono signal is first decomposed into multiple sound sources by the use of high-rank nonnegative matrix factorization. Then the multiple sources are clustered into two groups based on tonality criterion. In synthesis phase, one group is directly fed into left and right channels while the other group is decorrelated before being fed into each channel. Subjective tests reveals that the proposed method gives listener high SIQ and large ASW with minimizing timbral distortions.

An Implementation of Story Path Recommendation System of Interactive Drama Using PCA and NMF (PCA와 NMF를 이용한 대화식 드라마의 스토리 경로 추천 시스템 구현)

  • Lee, Yeon-Chang;Jang, Jae-Hee;Kim, Myung-Gwan
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.12 no.4
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    • pp.95-102
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    • 2012
  • Interactive drama is a story which requires user's free choice and participation. In this study, we grasp user's preference by making training data that utilize characters of interactive drama. Furthermore, we describe process of implementing systems which recommend new users path of stories that correspond with their preference. We used PCA and NMF to extract characteristic of preference. The success rate of recommending was 75% with PCA, while 62.5% with NMF.

Statistical Voice Activity Detection Using Probabilistic Non-Negative Matrix Factorization (확률적 비음수 행렬 인수분해를 사용한 통계적 음성검출기법)

  • Kim, Dong Kook;Shin, Jong Won;Kwon, Kisoo;Kim, Nam Soo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.41 no.8
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    • pp.851-858
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    • 2016
  • This paper presents a new statistical voice activity detection (VAD) based on the probabilistic interpretation of nonnegative matrix factorization (NMF). The objective function of the NMF using Kullback-Leibler divergence coincides with the negative log likelihood function of the data if the distribution of the data given the basis and encoding matrices is modeled as Poisson distributions. Based on this probabilistic NMF, the VAD is constructed using the likelihood ratio test assuming that speech and noise follow Poisson distributions. Experimental results show that the proposed approach outperformed the conventional Gaussian model-based and NMF-based methods at 0-15 dB signal-to-noise ratio simulation conditions.

A New Anchor Shot Detection System for News Video Indexing

  • Lee, Han-Sung;Im, Young-Hee;Park, Joo-Young;Park, Dai-Hee
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2007.11a
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    • pp.217-220
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    • 2007
  • In this paper, we present a new anchor shot detection system which is a core step of the preprocessing process for the news video analysis. The proposed system is composed of four modules and operates sequentially: 1) skin color detection module for reducing the candidate face regions; 2) face detection module for finding the key-frames with a facial data; 3) vector representation module for the key-frame images using a non-negative matrix factorization; 4) anchor shot detection module using a support vector data description. According to our computer experiments, the proposed system shows not only the comparable accuracy to the recent other results, but also more faster detection rate than others.

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Speech Denoising via Low-Rank and Sparse Matrix Decomposition

  • Huang, Jianjun;Zhang, Xiongwei;Zhang, Yafei;Zou, Xia;Zeng, Li
    • ETRI Journal
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    • v.36 no.1
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    • pp.167-170
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    • 2014
  • In this letter, we propose an unsupervised framework for speech noise reduction based on the recent development of low-rank and sparse matrix decomposition. The proposed framework directly separates the speech signal from noisy speech by decomposing the noisy speech spectrogram into three submatrices: the noise structure matrix, the clean speech structure matrix, and the residual noise matrix. Evaluations on the Noisex-92 dataset show that the proposed method achieves a signal-to-distortion ratio approximately 2.48 dB and 3.23 dB higher than that of the robust principal component analysis method and the non-negative matrix factorization method, respectively, when the input SNR is -5 dB.

Dimension-Reduced Audio Spectrum Projection Features for Classifying Video Sound Clips

  • Kim, Hyoung-Gook
    • The Journal of the Acoustical Society of Korea
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    • v.25 no.3E
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    • pp.89-94
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    • 2006
  • For audio indexing and targeted search of specific audio or corresponding visual contents, the MPEG-7 standard has adopted a sound classification framework, in which dimension-reduced Audio Spectrum Projection (ASP) features are used to train continuous hidden Markov models (HMMs) for classification of various sounds. The MPEG-7 employs Principal Component Analysis (PCA) or Independent Component Analysis (ICA) for the dimensional reduction. Other well-established techniques include Non-negative Matrix Factorization (NMF), Linear Discriminant Analysis (LDA) and Discrete Cosine Transformation (DCT). In this paper we compare the performance of different dimensional reduction methods with Gaussian mixture models (GMMs) and HMMs in the classifying video sound clips.

Language Model Adaptation Based on Topic Probability of Latent Dirichlet Allocation

  • Jeon, Hyung-Bae;Lee, Soo-Young
    • ETRI Journal
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    • v.38 no.3
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    • pp.487-493
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    • 2016
  • Two new methods are proposed for an unsupervised adaptation of a language model (LM) with a single sentence for automatic transcription tasks. At the training phase, training documents are clustered by a method known as Latent Dirichlet allocation (LDA), and then a domain-specific LM is trained for each cluster. At the test phase, an adapted LM is presented as a linear mixture of the now trained domain-specific LMs. Unlike previous adaptation methods, the proposed methods fully utilize a trained LDA model for the estimation of weight values, which are then to be assigned to the now trained domain-specific LMs; therefore, the clustering and weight-estimation algorithms of the trained LDA model are reliable. For the continuous speech recognition benchmark tests, the proposed methods outperform other unsupervised LM adaptation methods based on latent semantic analysis, non-negative matrix factorization, and LDA with n-gram counting.

Extraction of Image Bases based on Non-Negative Matrix Factorization for Visual Stimuli Reconstruction (시각 영상 복원을 위한 비음수 행렬 분해 기반의 영상 기저 추출)

  • Park, Young-Myo;Cho, Seong-Sik;Shin, Bong-Ki;Lee, Seong-Whan
    • Proceedings of the Korean Information Science Society Conference
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    • 2011.06c
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    • pp.383-385
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    • 2011
  • 본 논문에서는 동영상을 대상으로 하는 기존의 시각주의 시스템의 성능을 향상시킨 새로운 시스템에 대하여 설명한다. 제안하는 시스템은 기존의 시스템이 가지고 있던 한계점인 서로 반대되는 특징을 가지는 색상에서 하나의 특징만을 고정적으로 선택하던 것을 극복하여, 서로 반대되는 특징 중 현저함이 더 높은 색상 특징을 선택하여 입력 들어오는 영상에 적응적인 현저함 추출을 하였다. 도한 시간 현저함 정보를 추가적으로 고려할 수 있도록 하여 동영상에 대한 처리도 가능하도록 하였고, 성능 평가 시 인간을 대상으로 한 설문 조사 실험을 추가하여 보다 인간의 시각 인식과 유사한 시스템임을 증명하였다.