• 한국컴퓨터정보학회논문지
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• 제23권12호
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• pp.219-226
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• 2018

Collaborative filtering has been most popular approach to recommend items in online recommender systems. However, collaborative filtering is known to suffer from data sparsity problem. As a simple way to overcome this problem in literature, Jaccard index has been adopted to combine with the existing similarity measures. We analyze performance of such combination in various data environments. We also find optimal weights of factors in the combination using a genetic algorithm to formulate a similarity measure. Furthermore, optimal weights are searched for each user independently, in order to reflect each user's different rating behavior. Performance of the resulting personalized similarity measure is examined using two datasets with different data characteristics. It presents overall superiority to previous measures in terms of recommendation and prediction qualities regardless of the characteristics of the data environment.

• 융합신호처리학회논문지
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• 제10권2호
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• pp.132-135
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• 2009

비디오 데이터들의 효율적 색인과 검색을 위해서는 비디오 시퀀스의 유사도 측정방법이 매우 중요한 요소이다. 본 논문은 비디오 시퀀스에 대한 효율적인 유사도 측정을 위해 휘도 성분 투사법을 제안한다. 기존의 알고리즘들이 히스토그램, 윤곽선, 움직임등과 같은 특성을 사용한 반면 본 논문에서 제안한 알고리즘은 휘도 성분을 투사하는 방법을 사용하여 비디오 유사도 특성을 효율적으로 나타낼 수 있다. 비디오 데이터의 효율적인 색인과 계산량 감소를 위해 누적된 유사도에 의해 추출된 키프레임들을 이용하여 비디오 시퀀스의 유사도를 구하고 수정된 하우스도르프 거리를 사용하여 키프레임 묶음들의 유사도를 측정하였다. 실험결과 제안한 휘도투시법을 사용한 비디오 색인 기법이 유사도 특성에서 기존의 특성을 사용한 방법에 비해 확연한 정확도 및 성능 차이를 보였다.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제7권5호
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• pp.440-450
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• 2001

본 논문에서는 MPEG 비디오의 급진적 장면전환검출을 위한 효과적인 알고리즘을 제안한다. 제안된 알고리즘은 각 프레임들의 DC 계수만을 디코딩하여 DV 영상을 추출한다. 그리고 두 DC 영상 사이에서 움직임 예측을 시행함으로써 크기가 줄어든 DC 영상에 적합한 움직임 벡터를 찾아내며, 얻어진 DC 영상과 움직임 벡터를 이용하여 프레임 사이의 유사도 측정치를 구함으로써 장면전환을 검출한다. 본 논문에서 제안하는 알고리즘은 인접한 프레임 사이의 유사도 측정치, 즉 움직임 보상된 프레임간 상관계수를 계산하고 이를 시퀀스에 관계없이 동일한 값을 가지는 임계값과 비교함으로써 장면전환을 검출한다. 실험 겨로가, 제안된 알고리즘은 대부분의 시퀀스에서 90% 이상의 ‘recall’과 ‘precision’을 나타내었으며 시퀀스에 따라 서로 다른 임계값을 사용하는 기존의 알고리즘들보다 더 좋은 결과를 나타내었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권6호
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• pp.3121-3143
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• 2019

Segmentation plays an important role in the field of image processing and computer vision. Intuitionistic fuzzy C-means (IFCM) clustering algorithm emerged as an effective technique for image segmentation in recent years. However, standard fuzzy C-means (FCM) and IFCM algorithms are sensitive to noise and initial cluster centers, and they ignore the spatial relationship of pixels. In view of these shortcomings, an improved algorithm based on IFCM is proposed in this paper. Firstly, we propose a modified non-membership function to generate intuitionistic fuzzy set and a method of determining initial clustering centers based on grayscale features, they highlight the effect of uncertainty in intuitionistic fuzzy set and improve the robustness to noise. Secondly, an improved nonlinear kernel function is proposed to map data into kernel space to measure the distance between data and the cluster centers more accurately. Thirdly, the local spatial-gray information measure is introduced, which considers membership degree, gray features and spatial position information at the same time. Finally, we propose a new measure of intuitionistic fuzzy entropy, it takes into account fuzziness and intuition of intuitionistic fuzzy set. The experimental results show that compared with other IFCM based algorithms, the proposed algorithm has better segmentation and clustering performance.

• 정보처리학회논문지B
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• 제11B권7호
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• pp.867-874
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• 2004

K-Means 알고리즘은 재배치 기법의 일종으로 K개의 초기 센트로이드를 중심으로 K개의 클러스터가 될 때까지 클러스터링을 반복하는 것이다. 알고리즘의 특성상 K-Means 알고리즘은 초기 클러스터 센트로이드(중심) 및 클러스터 중심을 결정하는 방법에 따라 다른 클러스터링 결과를 얻을 수 있다. 본 논문에서는 K-Means 알고리즘을 이용한 초기 클러스터 중심 및 클러스터 중심을 결정하는 방법을 개선한 변형 K-Means 알고리즘을 제안한다. 제안한 알고리즘의 평가를 위하여 SMART 시스템의 16가지 가중치 계산 방식을 이용하여 성능을 평가한 결과 변형 K-Means알고리즘이 K-Means 알고리즘보다 재현률과 F-Measure에서 $20{\%}$이상 향상된 결과를 얻을 수 있었으며 특정 주제 아래 관련 문서가 할당되는 클러스터링 성능이 우수함을 알 수 있었다.

• 음성과학
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• 제10권2호
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• pp.77-84
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• 2003

In this research, we seek the beginning of the speech and detect the stationary speech region using lip information. Performing running average of the estimated speech signal in the stationary region, we reduce the effect of musical noise which is inherent to the conventional MlMSE (Minimum Mean Square Error) speech enhancement algorithm. In addition to it, SFM (Spectral Flatness Measure) is incorporated to reduce the speech signal estimation error due to speaking habit and some lacking lip information. The proposed algorithm with Wiener filtering shows the superior performance to the conventional methods according to MOS (Mean Opinion Score) test.

• Journal of applied mathematics & informatics
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• 제30권1_2호
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• pp.1-14
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• 2012

In this paper, the problem of determining the optimal car-deriving strategy has been examined. In order to find the optimal driving strategy, we have modified a method based on measure theory. Further, we demonstrate that the modified method is an efficient and practical algorithm for dealing with optimal control problems in a canonical formulation.

• 한국통신학회논문지
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• 제22권6호
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• pp.1195-1209
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• 1997

This paper presents a new morphological spatio-temporal segmentation algorithm. The algorithm incorporates luminance and motion information simultaneously, and uses morphological tools such as morphological filtersand watershed algorithm. The procedure toward complete segmentation consists of three steps:joint marker extraction, boundary decision, and motion-based region fusion. First, the joint marker extraction identifies the presence of homogeneours regions in both motion and luminance, where a simple joint marker extraction technique is proposed. Second, the spatio-temporal boundaries are decided by the watershed algorithm. For this purposek, a new joint similarity measure is proposed. Finally, an elimination ofredundant regions is done using motion-based region function. By incorporating spatial and temporal information simultaneously, we can obtain visually meaningful segmentation results. Simulation results demonstratesthe efficiency of the proposed method.

• 한국경영과학회지
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• 제14권1호
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• pp.36-50
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• 1989

The purpose of this study is to design effective working systems which adapt to changes in human needs by developing an algorithm which forms workers into optimal groups using the meausre of cohesion. Three major results can be derived from the study. Firstly, the algorithm developed here provides an optimal point at which to stop clustering. Secondely, the entropic measure of cohesion having an internal probabilistic structure is superior with respect to any other methods proposed before as far as the design of workgroup is concerned. Thirdly, the r $C_{n}$ clustering algorithm is better than the dichotonomic one.e.

• 대한기계학회논문집A
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• 제31권3호
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• pp.395-402
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• 2007

This paper considers about the tilt measurement of lens module with multiple lenses. The tilt between lenses in lens module and barrel or between image sensor and barrel can be measured precisely with the proposed algorithm. The magnitude and direction of the tilt vector of lens and image sensor can be measured from the best focal surface. The selecting and setting of image sensor, test chart, image sensor centering to lens module, axis align, focus measure method are also explained to get highly precise tilt results. The proposed algorithm is verified with the lens module inspection system we developed, and the experimental results show that the tilt measure proposed in this paper is robust and precise. With the proposed tilt measurement algorithm, the tilt of an image sensor and any other lens which intermediates light can be measured.