• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.608-616
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• 2001

This paper proposes a face recognition technique that effectively combines fixed graph matching (FGM) and Fisherface algorithm. EGM as one of dynamic link architecture uses not only face-shape but also the gray information of image, and Fisherface algorithm as a class specific method is robust about variations such as lighting direction and facial expression. In the proposed face recognition adopting the above two methods, linear projection per node of an image graph reduces dimensionality of labeled graph vector and provides a feature space to be used effectively for the classification. In comparison with a conventional EGM, the proposed approach could obtain satisfactory results in the perspectives of recognition speeds. Especially, we could get higher average recognition rate of 90.1% than the conventional methods by hold-out method for the experiments with the Yale Face Databases and Olivetti Research Laboratory (ORL) Databases.

• Journal of Korea Multimedia Society
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• v.17 no.1
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• pp.34-42
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• 2014

In this paper an embedding algorithm based on commute time is implemented by organizing patches according to the graph-based metric, and its performance is analyzed by comparing with the results of principal component analysis embedding. It is usual that the dimensionality reduction be done within some acceptable approximation error. However this paper shows the proposed manifold embedding method generates the intrinsic geometry corresponding to the signal despite severe approximation error, so that it can be applied to the areas such as pattern classification or machine learning.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.547-552
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• 2014

As social multimedia sites are getting popular such as Flickr and Facebook, the amount of image information has been increasing very fast. So there have been many studies for accurate social image retrieval. Some of them were web image classification using semantic relations of image tags and BoVW(Bag of Visual Words). In this paper, we propose a method to detect salient region in images using GBVS(Graph Based Visual Saliency) model which can eliminate less important region like a background. First, We construct BoVW based on SIFT algorithm from the database of the preliminary retrieved images with semantically related tags. Second, detect salient region in test images using GBVS model. The result of image classification showed higher accuracy than the previous research. Therefore we expect that our method can classify a variety of images more accurately.

• Tunnel and Underground Space
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• v.28 no.4
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• pp.358-371
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• 2018

The stability of openings in the underground mine is major concern in the operation of mines that must ensure productivity and safety. Among many rock conditions affecting cavities stability, the width and height of the opening is an important design factor. In this paper, we consider to determine the maximum unsupported span of a opening in a limestone mine by using the Q system among several rock classification schemes. In order to determine the span of the unsupported opening in the limestone mine, rock mass classifications were carried out at over 200 sites in the underground limestone mines. The relationships by using the Q system and the stability graph proposed by Mathews to determine the maximum span of the unsupported opening were derived and compared. We propose a new classification method that combines GSI and RMR rock classification systems to make it easy to use in a field.

• Journal of Korea Multimedia Society
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• v.13 no.8
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• pp.1183-1193
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• 2010

The pyramid graph is well known in parallel processing as a interconnection network topology based on regular square mesh and tree architectures. The enhanced pyramid graph is an alternative architecture by exchanging mesh into the corresponding torus on the base for upgrading performance than the pyramid. In this paper, we adopt a strategy of classification into two disjoint groups of edges in regular square torus as a basic sub-graph constituting of each layer in the enhanced pyramid graph. Edge set in the torus graph is considered as two disjoint sub-sets called NPC(represents candidate edge for neighbor-parent) and SPC(represents candidate edge for shared-parent) whether the parents vertices adjacent to two end vertices of the corresponding edge have a relation of neighbor or sharing in the upper layer of the enhanced pyramid graph. In addition, we also introduce a notion of shrink graph to focus only on the NPC-edges by hiding SPC-edges within the shrunk super-vertex on the resulting shrink graph. In this paper, we analyze that the lower and upper bounds on the number of NPC-edges in a Hamiltonian cycle constructed on $2^n{\times}2^n$ torus is $2^{2n-2}$ and $3{\cdot}2^{2n-2}$ respectively. By expanding this result into the enhanced pyramid graph, we also prove that the maximum number of NPC-edges containable in a Hamiltonian cycle is $4^{n-1}$-2n+1 in the n-dimensional enhanced pyramid.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.4
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• pp.242-249
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• 2011

In this study, The degree of scoliosis, an algorithm that can automatically detect was developed. Developed system was used for X-ray imaging source. The formula for the degree of curvature of the spine of the S <0, and, L> 0 is satisfied with the condition $Y=SX^2+L$ is a function expression. X-axis length can be changed and applied equally in all spline function graph, and the slope is $S=-L/92^2$. The graph on the degree of scoliosis of the differential equation Y'= 2SX could see that the extracted spine wire for the classification and the classification of scoliosis, the degree is determined as the available algorithms.

• Korean Journal of Construction Engineering and Management
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• v.22 no.1
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• pp.106-115
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• 2021

Classifying work trades using images can serve an important role in a multitude of advanced applications in construction management and automated progress monitoring. However, images obtained from work sites may not always be clean. Defective images can damage an image classifier's accuracy which gives rise to a needs for a method to enhance a work trade image classifier's performance. We propose a method that uses work dependency information to aid image classifiers. We show that using work dependency can enhance the classifier's performance, especially when a base classifier is not so great in doing its job.

• Journal of Sasang Constitutional Medicine
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• v.8 no.2
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• pp.191-202
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• 1996

Sasang constitution classification is very important subject, so many medical men studied the Sasang constitution classification but there is no certain method to classify objectively. And the purpose of this study is to help classifying Sasang constitution through correlation with sound spectrogram. This study was done it under the suppose that Sasang costitution hag correlation with sound spectrogram. The following results were obtained about correlation between sound spectrogram and Sasang constitution by comparison and analysis the pitch and reading speed of Sasang constitutions; 1. There was a similar tendency in the composition reading speed between taeeumin, soeumin and soyangin. 2. Taeeumin's center was lower measured more than soeumin's and soyangin's in the pitch graph and graph by normal curve fit and there was a similar tendency between soeumin and soyangin. 3. There was a similar tendency in the pitch graph's width between all constitutions. 4. There was a significant difference between taeeumin and soeum in the mean of three constitution's pitch, this means that taeeumin uses lower voice more than soeumin. According to the results, it is considered that there is a correlation between pitch of sound spectrogram and Sasang constitution. And method of Sasang constitution classification through sound spectrogram analysis can be one method as assistant for the objectification of Sasang constitution classification.

• Journal of KIISE
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• v.45 no.1
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• pp.15-21
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• 2018

Low-Rank Representation (LRR) based methods are widely used in many practical applications, such as face clustering and object detection, because they can guarantee high prediction accuracy when used to constructing graphs in graph - based semi-supervised learning. However, in order to solve the LRR problem, it is necessary to perform singular value decomposition on the square matrix of the number of data points for each iteration of the algorithm; hence the calculation is inefficient. To solve this problem, we propose an improved and faster LRR method based on the recently published Fast LRR (FaLRR) and suggests ways to introduce and optimize additional constraints on the underlying optimization goals in order to address the fact that the FaLRR is fast but actually poor in classification problems. Our experiments confirm that the proposed method finds a better solution than LRR does. We also propose Fast MLRR (FaMLRR), which shows better results when the goal of minimizing is added.

• Nuclear Engineering and Technology
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• v.47 no.2
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• pp.176-186
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• 2015

Pattern classifications have become important tools for fault diagnosis in nuclear power plants (NPP). However, it is often difficult to obtain training data under fault conditions to train a supervised classification model. By contrast, normal plant operating data can be easily made available through increased deployment of supervisory, control, and data acquisition systems. Such data can also be used to train classification models to improve the performance of fault diagnosis scheme. In this paper, a fault diagnosis scheme based on semisupervised classification (SSC) scheme is developed. In this scheme, new measurements collected from the plant are integrated with data observed under fault conditions to train the SSC models. The trained models are subsequently applied to new measurements for fault diagnosis. In comparison with supervised classifiers, the proposed scheme requires significantly fewer data collected under fault conditions to train the classifier. The developed scheme has been validated using different fault scenarios on a desktop NPP simulator as well as on a physical NPP simulator using a graph-based SSC algorithm. All the considered faults have been successfully diagnosed. The results have demonstrated that SSC is a promising tool for fault diagnosis in NPPs.