• 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.

• Journal of Digital Convergence
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• v.11 no.6
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• pp.199-204
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• 2013

Speech recognition system is shall be composed model of learning from the inaccurate input speech. Similar phoneme models to recognize, because it leads to the recognition rate decreases. Therefore, in this paper, we propose a method of speech recognition optimal learning model configuration using the Bhattacharyya algorithm. Based on feature of the phonemes, HMM feature extraction method was used for the phonemes in the training data. Similar learning model was recognized as a model of exact learning using the Bhattacharyya algorithm. Optimal learning model configuration using the Bhattacharyya algorithm. Recognition performance was evaluated. In this paper, the result of applying the proposed system showed a recognition rate of 98.7% in the speech recognition.

• Proceedings of the IEEK Conference
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• 1999.06a
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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.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.353-358
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• 2015

The Vocabulary Recognition System made by recognizing the standard vocabulary is seen as a decline of recognition when out of the standard or similar words. In this case, reconstructing the system in order to add or extend a range of vocabulary is a way to solve the problem. This paper propose configured Bhattacharyya algorithm standing by speech recognition learning model using the Bayesian methods which reflect parameter estimation upon the model configuration scalability. It is recognized corrected standard model based on a characteristic of the phoneme using the Bayesian methods for parameter estimation of the phoneme's data and Bhattacharyya algorithm for a similar model. By Bhattacharyya algorithm to configure recognition model evaluates a recognition performance. The result of applying the proposed method is showed a recognition rate of 97.3% and a learning curve of 1.2 seconds.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3D
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• pp.419-431
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• 2009

The core of this study is finding out the efficient band selection or extraction method discovering the optimal spectral bands when applying canonical correlation classifier (CCC) to hyperspectral data. The optimal efficient bands grounded on each separability decision technique are selected using Multispec$^{(C)}$ software developed by Purdue university of USA. Total 6 separability decision techniques are used, which are Divergence, Transformed Divergence, Bhattacharyya, Mean Bhattacharyya, Covariance Bhattacharyya, Noncovariance Bhattacharyya. For feature extraction, PCA transformation and MNF transformation are accomplished by ERDAS Imagine and ENVI software. For the comparison and assessment on the effect of feature selection and feature extraction, land cover classification is performed by CCC. The overall accuracy of CCC using the firstly selected 60 bands is 71.8%, the highest classification accuracy acquired by CCC is 79.0% as the case that executes CCC after appling Noncovariance Bhattacharyya. In conclusion, as a matter of fact, only Noncovariance Bhattacharyya separability decision method was valuable as feature selection algorithm for hyperspectral image classification depended on CCC. The lassification accuracy using other feature selection and extraction algorithms except Divergence rather declined in CCC.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.1-10
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• 2012

In this paper, we propose a new class of color histogram model suitable for object tracking. In addition to the pixel count, each bin of the proposed model also contains the spatial mean and the average value of the pixels located at a certain distance from the mean location of the bin. Using the proposed color histogram model, we derive a mean shift procedure using the modified Bhattacharyya distance. Unlike most mean shift based methods, our algorithm performs well even when the object being tracked shares similar colors with the background. Experimental results demonstrate improved tracking performance over existing methods.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.165-170
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• 2015

The Vocabulary Recognition System made by recognizing the standard vocabulary is seen as a decline of recognition when out of the standard or similar words. The vector values of the existing system to the model created by configuring the database was used in the recognition vocabulary. The model to be formed during the search for the recognition vocabulary is recognizable because there is a disadvantage not configured with a database. In this paper, it induced to recognize the vector model is formed by the search and configuration using a Bayesian model recognizes the Bhattacharyya distance measurement based on the vector model, by applying the Wiener filter improves the recognition rate. The result of Convergence of two method's are improved reliability experiments for distance measurement. Using a proposed measurement are compared to the conventional method exhibited a performance of 98.2%.

• Journal of Broadcast Engineering
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• v.15 no.2
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• pp.236-247
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• 2010

Recently, mean shift tracking algorithms have been proposed which use the information of color histogram together with some spatial information provided by the kernel. In spite of their fast speed, the algorithms are suffer from an inherent instability problem which is due to the use of an isotropic kernel for spatiality and the use of the Bhattacharyya coefficient as a similarity function. In this paper, we analyze how the kernel and the Bhattacharyya coefficient can arouse the instability problem. Based on the analysis, we propose a novel tracking scheme that uses a new representation of the location of the target which is constrained by the color, the area, and the spatiality information of the target in a more stable way than the mean shift algorithm. With this representation, the target localization in the next frame can be achieved by one step computation, which makes the tracking stable, even in difficult situations such as low-rate-frame environment, and partial occlusion.

• ETRI Journal
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• v.34 no.3
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• pp.399-409
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• 2012

In this paper, we propose a new color histogram model for object tracking. The proposed model incorporates the color arrangement of the target that encodes the relative spatial distribution of the colors inside the object. Using the color arrangement, we can determine which color bin is more reliable for tracking. Based on the proposed color histogram model, we derive a mean shift framework using a modified Bhattacharyya distance. In addition, we present a method of updating an object scale and a target model to cope with changes in the target appearance. Unlike conventional mean shift based methods, our algorithm produces satisfactory results even when the object being tracked shares similar colors with the background.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.6
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• pp.91-98
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• 2011

This paper proposes an efficient and accurate vision based recognition and tracking framework for texture free objects. We approached this problem with a two phased algorithm: detection phase and tracking phase. In the detection phase, the algorithm extracts shape context descriptors that used for classifying objects into predetermined interesting targets. Later on, the matching result is further refined by a minimization technique. In the tracking phase, we resorted to meanshift tracking algorithm based on Bhattacharyya coefficient measurement. In summary, the contributions of our methods for the underwater robot vision are four folds: 1) Our method can deal with camera motion and scale changes of objects in underwater environment; 2) It is inexpensive vision based recognition algorithm; 3) The advantage of shape based method compared to a distinct feature point based method (SIFT) in the underwater environment with possible turbidity variation; 4) We made a quantitative comparison of our method with a few other well-known methods. The result is quite promising for the map based underwater SLAM task which is the goal of our research.