• 제목/요약/키워드: Nearest neighbor distance Method

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Facial Expression Recognition using ICA-Factorial Representation Method (ICA-factorial 표현법을 이용한 얼굴감정인식)

  • Han, Su-Jeong;Kwak, Keun-Chang;Go, Hyoun-Joo;Kim, Sung-Suk;Chun, Myung-Geun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.13 no.3
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    • pp.371-376
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    • 2003
  • In this paper, we proposes a method for recognizing the facial expressions using ICA(Independent Component Analysis)-factorial representation method. Facial expression recognition consists of two stages. First, a method of Feature extraction transforms the high dimensional face space into a low dimensional feature space using PCA(Principal Component Analysis). And then, the feature vectors are extracted by using ICA-factorial representation method. The second recognition stage is performed by using the Euclidean distance measure based KNN(K-Nearest Neighbor) algorithm. We constructed the facial expression database for six basic expressions(happiness, sadness, angry, surprise, fear, dislike) and obtained a better performance than previous works.

Appearance-based Object Recognition Using Higher Order Local Auto Correlation Feature Information (고차 국소 자동 상관 특징 정보를 이용한 외관 기반 객체 인식)

  • Kang, Myung-A
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.15 no.7
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    • pp.1439-1446
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    • 2011
  • This paper describes the algorithm that lowers the dimension, maintains the object recognition and significantly reduces the eigenspace configuration time by combining the higher correlation feature information and Principle Component Analysis. Since the suggested method doesn't require a lot of computation than the method using existing geometric information or stereo image, the fact that it is very suitable for building the real-time system has been proved through the experiment. In addition, since the existing point to point method which is a simple distance calculation has many errors, in this paper to improve recognition rate the recognition error could be reduced by using several successive input images as a unit of recognition with K-Nearest Neighbor which is the improved Class to Class method.

An Efficient Range Search and Nearest Neighbor Search Algorithm for Action Parts of Active Systems in Sparse Area (능동 시스템에서 위치관련 액션 수행을 위한 희소공간 공간객체의 효율적인 영역질의와 최근접질의)

  • Kim, Jung-Il;Hong, Dong-Kweon
    • The KIPS Transactions:PartD
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    • v.8D no.2
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    • pp.125-131
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    • 2001
  • Various kind of disasters happens in our society. Most of them require immediate treatment to save life or to protect valuable products. When an accident happens in a place, it is reported to the headquarter of emergency measures system. According to the nature of accident several treatments orders are transmitted to the related authorities. In this paper, we introduce an intelligent emergency measures system that uses trigger mechanism of active databases. The system responds to various events spontaneously without intervention of mankind by triggering proper rules. The most important part of an action in the system is the capability of searching places to apply adequate treatments quickly. We have developed a new method for range queries and nearest neighbor queries which utilize the z-ordering technique to get fast responses. Those new methods are further extended to handle more realistic actual distance of road among positions.

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SMOTE by Mahalanobis distance using MCD in imbalanced data (불균형 자료에서 MCD를 활용한 마할라노비스 거리에 의한 SMOTE)

  • Jieun Jung;Yong-Seok Choi
    • The Korean Journal of Applied Statistics
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    • v.37 no.4
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    • pp.455 -465
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    • 2024
  • SMOTE (synthetic minority over-sampling technique) has been used the most as a solution to the problem of imbalanced data. SMOTE selects the nearest neighbor based on Euclidean distance. However, Euclidean distance has the disadvantage of not considering the correlation between variables. In particular, the Mahalanobis distance has the advantage of considering the covariance of variables. But if there are outliers, they usually influence calculating the Mahalanobis distance. To solve this problem, we use the Mahalanobis distance by estimating the covariance matrix using MCD (minimum covariance determinant). Then apply Mahalanobis distance based on MCD to SMOTE to create new data. Therefore, we showed that in most cases this method provided high performance indicators for classifying imbalanced data.

Enhancing Classification Performance of Temporal Keyword Data by Using Moving Average-based Dynamic Time Warping Method (이동 평균 기반 동적 시간 와핑 기법을 이용한 시계열 키워드 데이터의 분류 성능 개선 방안)

  • Jeong, Do-Heon
    • Journal of the Korean Society for information Management
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    • v.36 no.4
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    • pp.83-105
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    • 2019
  • This study aims to suggest an effective method for the automatic classification of keywords with similar patterns by calculating pattern similarity of temporal data. For this, large scale news on the Web were collected and time series data composed of 120 time segments were built. To make training data set for the performance test of the proposed model, 440 representative keywords were manually classified according to 8 types of trend. This study introduces a Dynamic Time Warping(DTW) method which have been commonly used in the field of time series analytics, and proposes an application model, MA-DTW based on a Moving Average(MA) method which gives a good explanation on a tendency of trend curve. As a result of the automatic classification by a k-Nearest Neighbor(kNN) algorithm, Euclidean Distance(ED) and DTW showed 48.2% and 66.6% of maximum micro-averaged F1 score respectively, whereas the proposed model represented 74.3% of the best micro-averaged F1 score. In all respect of the comprehensive experiments, the suggested model outperformed the methods of ED and DTW.

Gesture Recognition Using Higher Correlation Feature Information and PCA

  • Kim, Jong-Min;Lee, Kee-Jun
    • Journal of Integrative Natural Science
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    • v.5 no.2
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    • pp.120-126
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    • 2012
  • This paper describes the algorithm that lowers the dimension, maintains the gesture recognition and significantly reduces the eigenspace configuration time by combining the higher correlation feature information and Principle Component Analysis. Since the suggested method doesn't require a lot of computation than the method using existing geometric information or stereo image, the fact that it is very suitable for building the real-time system has been proved through the experiment. In addition, since the existing point to point method which is a simple distance calculation has many errors, in this paper to improve recognition rate the recognition error could be reduced by using several successive input images as a unit of recognition with K-Nearest Neighbor which is the improved Class to Class method.

Vantage Point Metric Index Improvement for Multimedia Databases

  • Chanpisey, Uch;Lee, Sang-Kon Samuel;Lee, In-Hong
    • Proceedings of the Korean Information Science Society Conference
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    • 2011.06c
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    • pp.112-114
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    • 2011
  • On multimedia databases, in order to realize the fast access method, indexing methods for the multidimension data space are used. However, since it is a premise to use the Euclid distance as the distance measure, this method lacks in flexibility. On the other hand, there are metric indexing methods which require only to satisfy distance axiom. Since metric indexing methods can also apply for distance measures other than the Euclid distance, these methods have high flexibility. This paper proposes an improved method of VP-tree which is one of the metric indexing methods. VP-tree follows the node which suits the search range from a route node at searching. And distances between a query and all objects linked from the leaf node which finally arrived are computed, and it investigates whether each object is contained in the search range. However, search speed will become slow if the number of distance calculations in a leaf node increases. Therefore, we paid attention to the candidates selection method using the triangular inequality in a leaf node. As the improved methods, we propose a method to use the nearest neighbor object point for the query as the datum point of the triangular inequality. It becomes possible to make the search range smaller and to cut down the number of times of distance calculation by these improved methods. From evaluation experiments using 10,000 image data, it was found that our proposed method could cut 5%~12% of search time of the traditional method.

An Online Forklift Dispatching Algorithm Based on Minimal Cost Assignment Approach (최소 비용할당 기반 온라인 지게차 운영 알고리즘)

  • kwon, BoBae;Son, Jung-Ryoul;Ha, Byung-Hyun
    • Journal of the Korea Society for Simulation
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    • v.27 no.2
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    • pp.71-81
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    • 2018
  • Forklifts in a shipyard lift and transport heavy objects. Tasks occur dynamically and the rate of the task occurrence changes over time. Especially, the rate of the task occurrence is high immediately after morning and afternoon business hours. The weight of objects varies according to task characteristic, and a forklift also has the workable or allowable weight limit. In this study, we propose an online forklift dispatching algorithm based on nearest-neighbor dispatching rule using minimal cost assignment approach in order to attain the efficient operations. The proposed algorithm considers various types of forklift and multiple jobs at the same time to determine the dispatch plan. We generate dummy forklifts and dummy tasks to handle unbalance in the numbers of forklifts and tasks by taking their capacity limits and weights. In addition, a method of systematic forklift selection is also devised considering the condition of the forklift. The performance indicator is the total travel distance and the average task waiting time. We validate our approach against the priority rule-based method of the previous study by discrete-event simulation.

An Improvement Video Search Method for VP-Tree by using a Trigonometric Inequality

  • Lee, Samuel Sangkon;Shishibori, Masami;Han, Chia Y.
    • Journal of Information Processing Systems
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    • v.9 no.2
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    • pp.315-332
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    • 2013
  • This paper presents an approach for improving the use of VP-tree in video indexing and searching. A vantage-point tree or VP-tree is one of the metric space-based indexing methods used in multimedia database searches and data retrieval. Instead of relying on the Euclidean distance as a measure of search space, the proposed approach focuses on the trigonometric inequality for compressing the search range, which thus, improves the search performance. A test result of using 10,000 video files shows that this method reduced the search time by 5-12%, as compared to the existing method that uses the AESA algorithm.

Person Recognition Using Gait and Face Features on Thermal Images (열 영상에서의 걸음걸이와 얼굴 특징을 이용한 개인 인식)

  • Kim, Sa-Mun;Lee, Dae-Jong;Lee, Ho-Hyun;Chun, Myung-Geun
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.65 no.2
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    • pp.130-135
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    • 2016
  • Gait recognition has advantage of non-contact type recognition. But It has disadvantage of low recognition rate when the pedestrian silhouette is changed due to bag or coat. In this paper, we proposed new method using combination of gait energy image feature and thermal face image feature. First, we extracted a face image which has optimal focusing value using human body rate and Tenengrad algorithm. Second step, we extracted features from gait energy image and thermal face image using linear discriminant analysis. Third, calculate euclidean distance between train data and test data, and optimize weights using genetic algorithm. Finally, we compute classification using nearest neighbor classification algorithm. So the proposed method shows a better result than the conventional method.