• The Transactions of the Korea Information Processing Society
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• v.4 no.1
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• pp.209-214
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• 1997

In this paper, we propose a new preorder tree method for binary images, named the Compact Complementary Quadtree (CCQ). In the proposed method we use type codes for representing nodes in the quadtree instead of using the symbols G, B, and W. From the experimental results, we have confirmed that the CCQ has a higher compression ratio than of the DF-expression. CCQ can be effectively applied to progressive transmission of binary images such as line drawings, geographical maps, and halftones.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1628-1639
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• 2017

The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low level texture features were combined in the approach. One of them was extracted from singular value decomposition (SVD) based dual-tree complex wavelet transform (DTCWT) coefficients, and the other one was extracted from multi-scale local binary patterns (LBPs). The fusion features of SVD based multi-directional wavelet features and multi-scale LBP features have short dimensions of feature vector. The comparing experiments are conducted on Brodatz and Vistex datasets. According to the experimental results, the proposed method has a relatively better performance in aspect of retrieval accuracy and time complexity upon the existing methods.

• Journal of Creative Information Culture
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• v.6 no.1
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• pp.41-48
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• 2020

In order to represent and process spatial data, hierarchical data structures such as a quadtree or a bintree are used. Various approaches for linearly representing the bintree have been proposed. S-Tree has the advantage of compressing the storage space by expressing binary region image data as a linear binary bit stream, but the higher the resolution of the image, the longer the length of the binary bit stream, the longer the storage space and the lower the search performance. In this paper, we construct a hierarchical structure of multiple separated bintrees with a full binary tree structure and express each bintree as two linear binary bit streams to reduce the range required for image search. It improves the overall search performance by performing a simple number conversion instead of searching directly the binary bit string path. Through the performance evaluation by the worst-case space-time complexity analysis, it was analyzed that the proposed method has better search performance and space efficiency than the previous one.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.99-108
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• 2004

In this paper, a new fast detection and clutter rejection method is proposed for CCD-image-based Automatic Target Detection System. For defence application, fast computation is a critical point, thus we concentrated on the ability to detect various targets with simple computation. In training stage, 1D template set is generated by regional vertical projection and K-means clustering, and binary tree structure is adopted to reduce the number of template matching in test stage. We also use adaptive skip-width by Correlation-based Adaptive Predictive Search(CAPS) to further improve the detecting speed. In clutter rejection stage, we obtain Fourier Descriptor coefficients from boundary information, which are useful to rejected clutters.

• ETRI Journal
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• v.43 no.2
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• pp.313-323
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• 2021

The Joint Video Exploration Team (JVET) has studied future video coding (FVC) technologies with a potential compression capacity that significantly exceeds that of the high-efficiency video coding (HEVC) standard. The joint exploration test model (JEM), a common platform for the exploration of FVC technologies in the JVET, employs quadtree plus binary tree block partitioning, which enhances the flexibility of coding unit partitioning. Despite significant improvement in coding efficiency for chrominance achieved by separating luminance and chrominance tree structures in I slices, this approach has intrinsic drawbacks that result in the redundancy of block partitioning data. In this paper, an adaptive tree structure correlating luminance and chrominance of single and dual trees is presented. Our proposed method resulted in an average reduction of -0.24% in the Y Bjontegaard Delta rate relative to the intracoding of JEM 6.0 common test conditions.

• ETRI Journal
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• v.32 no.5
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• pp.795-800
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• 2010

Embedding a large vocabulary speech recognition system in mobile devices requires a reduced acoustic model obtained by eliminating redundant model parameters. In conventional optimization methods based on the minimum description length (MDL) criterion, a binary Gaussian tree is built at each state of a hidden Markov model by iteratively finding and merging similar mixture components. An optimal subset of the tree nodes is then selected to generate a downsized acoustic model. To obtain a better binary Gaussian tree by improving the process of finding the most similar Gaussian components, this paper proposes a new distance measure that exploits the difference in likelihood values for cases before and after two components are combined. The mixture weight of Gaussian components is also introduced in the component merging step. Experimental results show that the proposed method outperforms MDL-based optimization using either a Kullback-Leibler (KL) divergence or weighted KL divergence measure. The proposed method could also reduce the acoustic model size by 50% with less than a 1.5% increase in error rate compared to a baseline system.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.14 no.1
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• pp.26-33
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• 2001

The proposed system was composed of pre-processor which was executing binary/high-pass filtering and post-processor which ranged from statistic data to prediction. In post-processor work, step one was filter process of image, step two was image recognition, and step three was destruction degree/time prediction. After these processing, we could predict image of the last destruction timestamp. This research was produced variation value according to growth of tree pattern. This result showed improved correction, when this research was applied image Processing. Pre-processing step of original image had good result binary work after high pas- filter execution. In the case of using partial discharge of the image, our research could predict the last destruction timestamp. By means of experimental data, this prediction system was acquired $\pm$3.2% error range.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.250-254
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• 2010

As the number of input and fuzzy set of a fuzzy system increase, the size of the rule base increases exponentially and becomes unmanageable (curse of dimensionality). In this paper, tree architectures of fuzzy controller (TAFC) is proposed to overcome the curse of dimensionality problem occurring in the design of fuzzy controller. TAFC is constructed with the aid of AND and OR fuzzy neurons. TAFC can guarantee reduced size of rule base with reasonable performance. For the development of TAFC, genetic algorithm constructs the binary tree structure by optimally selecting the nodes and leaves, and then random signal-based learning further refines the binary connections (two-step optimization). An inverted pendulum system is considered to verify the effectiveness of the proposed method by simulation.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.201-208
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• 2014

In this paper, we propose the classifier structure design algorithm using DTW. Proposed algorithm uses DTW result to design the binary tree architecture based on the SVM which classify the multi-class data. Design the binary tree architecture for Support Vector Machine(SVM-BTA) using the threshold criterion calculated by the sum columns in square matrix which components are the reference data from each class. For comparison the performance of the proposed algorithm, compare the results of classifiers which binary tree structure are designed based on database and k-means algorithm. The data used for classification is 333 signals from 18 classes of underwater transient noise. The proposed classifier has been improved classification performance compared with classifier designed by database system, and probability of detection for non-biological transient signal has improved compare with classifiers using k-means algorithm. The proposed SVM-BTA classified 68.77% of biological sound(BO), 92.86% chain(CHAN) the mechanical sound, and 100% of the 6 kinds of the other classes.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.2
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• pp.169-176
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• 2000

This paper is considered with the problem of embedding complete binary trees into 3-dimensional meshes using dimension-ordered routing with primary concern of minimizing link congestion. The authors showed that a complete binary tree with $2^P-1$ nodes can be embedded into a 3-dimensional mesh with optimum size, $2^P$ nodes, if the link congestion is two[14], (More precisely, the link congestion of each dimension is two, two, and one if the dimension-ordered routing is used, and two, one, and one if the dimension-ordered routing is not imposed.) In this paper, we present a scheme to find an embedding of a complete binary tree into a 3-dimensional mesh of size no larger than 1.27 times the optimum with link congestion one while using dimension-ordered routing.