• Title, Summary, Keyword: Bhattacharyya distance

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Error Estimation Based on the Bhattacharyya Distance for Classifying Multimodal Data (Multimodal 데이터에 대한 분류 에러 예측 기법)

  • Choe, Ui-Seon;Kim, Jae-Hui;Lee, Cheol-Hui
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.39 no.2
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    • pp.147-154
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    • 2002
  • In this paper, we propose an error estimation method based on the Bhattacharyya distance for multimodal data. First, we try to find the empirical relationship between the classification error and the Bhattacharyya distance. Then, we investigate the possibility to derive the error estimation equation based on the Bhattacharyya distance for multimodal data. We assume that the distribution of multimodal data can be approximated as a mixture of several Gaussian distributions. Experimental results with remotely sensed data showed that there exist strong relationships between the Bhattacharyya distance and the classification error and that it is possible to predict the classification error using the Bhattacharyya distance for multimodal data.

Estimation of Classification Error Based on the Bhattacharyya Distance for Data with Multimodal Distribution (Multimodal 분포 데이터를 위한 Bhattacharyya distance 기반 분류 에러예측 기법)

  • 최의선;이철희
    • Proceedings of the IEEK Conference
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    • pp.85-87
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    • 2000
  • In pattern classification, the Bhattacharyya distance has been used as a class separability measure and provides useful information for feature selection and extraction. In this paper, we propose a method to predict the classification error for multimodal data based on the Bhattacharyya distance. In our approach, we first approximate the pdf of multimodal distribution with a Gaussian mixture model and find the bhattacharyya distance and classification error. Exprimental results showed that there is a strong relationship between the Bhattacharyya distance and the classification error for multimodal data.

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Feature Extraction Method Using the Bhattacharyya Distance (Bhattacharyya distance 기반 특징 추출 기법)

  • Choi, Eui-Sun;Lee, Chul-Hee
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.37 no.6
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    • pp.38-47
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    • 2000
  • In pattern classification, the Bhattacharyya distance has been used as a class separability measure. Furthemore, it is recently reported that the Bhattacharyya distance can be used to estimate error of Gaussian ML classifier within 1-2% margin. In this paper, we propose a feature extraction method utilizing the Bhattacharyya distance. In the proposed method, we first predict the classification error with the error estimation equation based on the Bhauacharyya distance. Then we find the feature vector that minimizes the classification error using two search algorithms: sequential search and global search. Experimental reslts show that the proposed method compares favorably with conventional feature extraction methods. In addition, it is possible to determine how man, feature vectors arc needed for achieving the same classification accuracy as in the original space.

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Feature Extraction Method based on Bhattacharyya Distance for Multiclass Problems (Bhattacharyya Distance에 기반한 다중클래스 문제에 대한 피춰 추출 기법)

  • 최의선;이철희
    • Proceedings of the IEEK Conference
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    • pp.643-646
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    • 1999
  • In this paper, we propose a feature extraction method based on Bhattacharyya distance for multiclass problems. The Bhattacharyya distance provides a valuable information in determining the effectiveness of a feature set and has been used as separability measure for feature selection. Recently, a feature extraction algorithm hat been proposed for two normally distributed classes based on Bhattacharyya distance. In this paper, we propose to expand the previous approach to multiclass cases. Experiment results show that the proposed method compares favorably with the conventional methods.

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Edge Detection Based on Bhattacharyya Distance for Color Images Using Adaptive Boundary and Thresholding

  • Badripour, Afarin;Lee, Chulhee
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.944-945
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    • 2017
  • Color image edge detection is an important operation in many image processing areas. This paper presents a new method for edge detection based on the Bhattacharyya distance that can handle arbitrary boundaries by exploring several edge patterns. Experiments show promising results compared to some existing methods.

Phoneme Similarity Error Correction System using Bhattacharyya Distance Measurement Method (바타챠랴 거리 측정법을 이용한 음소 유사율 오류 보정 개선 시스템)

  • Ahn, Chan-Shik;Oh, Sang-Yeob
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.6
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    • pp.73-80
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    • 2010
  • Vocabulary recognition system is providing inaccurate vocabulary and similar phoneme recognition due to reduce recognition rate. It's require method of similar phoneme recognition unrecognized and efficient feature extraction process. Therefore in this paper propose phoneme likelihood error correction improvement system using based on phoneme feature Bhattacharyya distance measurement. Phoneme likelihood is monophone training data phoneme using HMM feature extraction method, similar phoneme is induced recognition able to accurate phoneme using Bhattacharyya distance measurement. They are effective recognition rate improvement. System performance comparison as a result of recognition improve represent 1.2%, 97.91% by Euclidean distance measurement and dynamic time warping(DTW) system.

Centroid Neural Network with Bhattacharyya Kernel (Bhattacharyya 커널을 적용한 Centroid Neural Network)

  • Lee, Song-Jae;Park, Dong-Chul
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.32 no.9C
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    • pp.861-866
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    • 2007
  • A clustering algorithm for Gaussian Probability Distribution Function (GPDF) data called Centroid Neural Network with a Bhattacharyya Kernel (BK-CNN) is proposed in this paper. The proposed BK-CNN is based on the unsupervised competitive Centroid Neural Network (CNN) and employs a kernel method for data projection. The kernel method adopted in the proposed BK-CNN is used to project data from the low dimensional input feature space into higher dimensional feature space so as the nonlinear problems associated with input space can be solved linearly in the feature space. In order to cluster the GPDF data, the Bhattacharyya kernel is used to measure the distance between two probability distributions for data projection. With the incorporation of the kernel method, the proposed BK-CNN is capable of dealing with nonlinear separation boundaries and can successfully allocate more code vector in the region that GPDF data are densely distributed. When applied to GPDF data in an image classification probleml, the experiment results show that the proposed BK-CNN algorithm gives 1.7%-4.3% improvements in average classification accuracy over other conventional algorithm such as k-means, Self-Organizing Map (SOM) and CNN algorithms with a Bhattacharyya distance, classed as Bk-Means, B-SOM, B-CNN algorithms.

SOME INEQUALITIES FOR THE $CSISZ{\acute{A}}R\;{\Phi}-DIVERGENCE$

  • Dragomir, S.S.
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.7 no.1
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    • pp.63-77
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    • 2003
  • Some inequalities for the $Csisz{\acute{a}}r\;{\Phi}-divergence$ and applications for the Kullback-Leibler, $R{\acute{e}}nyi$, Hellinger and Bhattacharyya distances in Information Theory are given.

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Improving Phoneme Recognition based on Gaussian Model using Bhattacharyya Distance Measurement Method (바타챠랴 거리 측정 기법을 사용한 가우시안 모델 기반 음소 인식 향상)

  • Oh, Sang-Yeob
    • Journal of Korea Multimedia Society
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    • v.14 no.1
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    • pp.85-93
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    • 2011
  • Previous existing vocabulary recognition programs calculate general vector values from a database, so they can not process phonemes that form during a search. And because they can not create a model for phoneme data, the accuracy of the Gaussian model can not secure. Therefore, in this paper, we recommend use of the Bhattacharyya distance measurement method based on the features of the phoneme-thus allowing us to improve the recognition rate by picking up accurate phonemes and minimizing recognition of similar and erroneous phonemes. We test the Gaussian model optimization through share continuous probability distribution, and we confirm the heighten recognition rate. The Bhattacharyya distance measurement method suggest in this paper reflect an average 1.9% improvement in performance compare to previous methods, and it has average 2.9% improvement based on reliability in recognition rate.

Centroid-model based music similarity with alpha divergence (알파 다이버전스를 이용한 무게중심 모델 기반 음악 유사도)

  • Seo, Jin Soo;Kim, Jeonghyun;Park, Jihyun
    • The Journal of the Acoustical Society of Korea
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    • v.35 no.2
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    • pp.83-91
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    • 2016
  • Music-similarity computation is crucial in developing music information retrieval systems for browsing and classification. This paper overviews the recently-proposed centroid-model based music retrieval method and applies the distributional similarity measures to the model for retrieval-performance evaluation. Probabilistic distance measures (also called divergence) compute the distance between two probability distributions in a certain sense. In this paper, we consider the alpha divergence in computing distance between two centroid models for music retrieval. The alpha divergence includes the widely-used Kullback-Leibler divergence and Bhattacharyya distance depending on the values of alpha. Experiments were conducted on both genre and singer datasets. We compare the music-retrieval performance of the distributional similarity with that of the vector distances. The experimental results show that the alpha divergence improves the performance of the centroid-model based music retrieval.