• Title/Summary/Keyword: similarity matching algorithm

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A Multi-Agent Improved Semantic Similarity Matching Algorithm Based on Ontology Tree (온톨로지 트리기반 멀티에이전트 세만틱 유사도매칭 알고리즘)

  • Gao, Qian;Cho, Young-Im
    • Journal of Institute of Control, Robotics and Systems
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    • v.18 no.11
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    • pp.1027-1033
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    • 2012
  • Semantic-based information retrieval techniques understand the meanings of the concepts that users specify in their queries, but the traditional semantic matching methods based on the ontology tree have three weaknesses which may lead to many false matches, causing the falling precision. In order to improve the matching precision and the recall of the information retrieval, this paper proposes a multi-agent improved semantic similarity matching algorithm based on the ontology tree, which can avoid the considerable computation redundancies and mismatching during the entire matching process. The results of the experiments performed on our algorithm show improvements in precision and recall compared with the information retrieval techniques based on the traditional semantic similarity matching methods.

A Study on Preprocessing Method for Effective Semantic-based Similarity Measures using Approximate Matching Algorithm (의미적 유사성의 효과적 탐지를 위한 데이터 전처리 연구)

  • Kang, Hari;Jeong, Doowon;Lee, Sangjin
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.25 no.3
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    • pp.595-602
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    • 2015
  • One of the challenges of the digital forensics is how to handle certain amounts of data efficiently. Although reliable and various approximate matching algorithms have been presented to quickly identify similarities between digital objects, its practical effectiveness to identify the semantic similarity is low because of frequent false positives. To solve this problem, we suggest adding a pre-processing of the approximate matching target dataset to increase matching accuracy while maintaining the reliability of the approximate matching algorithm. To verify the effectiveness, we experimented with two datasets of eml and hwp using sdhash in order to identify the semantic similarity.

AWGN Removal Algorithm using Similarity Determination of Block Matching (블록 매칭의 유사도 판별을 이용한 AWGN 제거 알고리즘)

  • Cheon, Bong-Won;Kim, Nam-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.11
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    • pp.1424-1430
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    • 2020
  • In this paper, we propose an algorithm to remove AWGN by considering the characteristics of noise present in the image. The proposed algorithm uses block matching to calculate the output, and calculates an estimate by determining the similarity between the center mask and the matching mask. The output of the filter is calculated by adding or subtracting the estimated value and the input pixel value, and weighting is given according to the standard deviation of the center mask and the noise constant to obtain the final output. In order to evaluate the proposed algorithm, the simulation was performed in comparison with the existing methods, and analyzed through the enlarged image and PSNR comparison. The proposed algorithm minimizes the effect of noise, preserves important characteristics of the image, and shows the performance of removing noise efficiently.

Ontology Matching Method Based on Word Embedding and Structural Similarity

  • Hongzhou Duan;Yuxiang Sun;Yongju Lee
    • International journal of advanced smart convergence
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    • v.12 no.3
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    • pp.75-88
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    • 2023
  • In a specific domain, experts have different understanding of domain knowledge or different purpose of constructing ontology. These will lead to multiple different ontologies in the domain. This phenomenon is called the ontology heterogeneity. For research fields that require cross-ontology operations such as knowledge fusion and knowledge reasoning, the ontology heterogeneity has caused certain difficulties for research. In this paper, we propose a novel ontology matching model that combines word embedding and a concatenated continuous bag-of-words model. Our goal is to improve word vectors and distinguish the semantic similarity and descriptive associations. Moreover, we make the most of textual and structural information from the ontology and external resources. We represent the ontology as a graph and use the SimRank algorithm to calculate the structural similarity. Our approach employs a similarity queue to achieve one-to-many matching results which provide a wider range of insights for subsequent mining and analysis. This enhances and refines the methodology used in ontology matching.

An Efficient Image Matching Scheme Based on Min-Max Similarity for Distorted Images (왜곡 영상을 위한 효과적인 최소-최대 유사도(Min-Max Similarity) 기반의 영상 정합 알고리즘)

  • Heo, Young-Jin;Jeong, Da-Mi;Kim, Byung-Gyu
    • Journal of Korea Multimedia Society
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    • v.22 no.12
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    • pp.1404-1414
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    • 2019
  • Educational books commonly use some copyrighted images with various kinds of deformation for helping students understanding. When using several copyrighted images made by merging or editing distortion in legal, we need to pay a charge to original copyright holders for each image. In this paper, we propose an efficient matching algorithm by separating each copyrighted image with the merged and edited type including rotation, illumination change, and change of size. We use the Oriented FAST and Rotated BRIEF (ORB) method as a basic feature matching scheme. To improve the matching accuracy, we design a new MIN-MAX similarity in matching stage. With the distorted dataset, the proposed method shows up-to 97% of precision in experiments. Also, we demonstrate that the proposed similarity measure also outperforms compared to other measure which is commonly used.

Stereo Matching using the Extended Edge Segments (확장형 에지 선소를 이용한 스테레오 정합)

  • Son, Hong-Rak;Kim, Hyeong-Seok
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.51 no.8
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    • pp.335-343
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    • 2002
  • A segment matching algorithm in stereo vision via the fusion of multiple features on long edge segments is proposed. One problem of the previous segment matching algorithm is the similarity among the segments caused from its short length. In the proposed algorithm, edges are composed of longer segments which are obtained by breaking the edges only at the locations with distinguished changes of the shape. Such long segments can contain extra features such as curvature ratio and length of segments which could not be included in shorter ones. Use of such additional features enhances the matching accuracy significantly To fuse multiple features for matching, weighting value determination algorithm which is computed according to the degree of the contribution of each factor is proposed. The stereo matching simulations with the proposed algorithm are done about various images and their results are included.

Improvement of ASIFT for Object Matching Based on Optimized Random Sampling

  • Phan, Dung;Kim, Soo Hyung;Na, In Seop
    • International Journal of Contents
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    • v.9 no.2
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    • pp.1-7
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    • 2013
  • This paper proposes an efficient matching algorithm based on ASIFT (Affine Scale-Invariant Feature Transform) which is fully invariant to affine transformation. In our approach, we proposed a method of reducing similar measure matching cost and the number of outliers. First, we combined the Manhattan and Chessboard metrics replacing the Euclidean metric by a linear combination for measuring the similarity of keypoints. These two metrics are simple but really efficient. Using our method the computation time for matching step was saved and also the number of correct matches was increased. By applying an Optimized Random Sampling Algorithm (ORSA), we can remove most of the outlier matches to make the result meaningful. This method was experimented on various combinations of affine transform. The experimental result shows that our method is superior to SIFT and ASIFT.

Globally Optimal Recommender Group Formation and Maintenance Algorithm using the Fitness Function (적합도 함수를 이용한 최적의 추천자 그룹 생성 및 유지 알고리즘)

  • Kim, Yong-Ku;Lee, Min-Ho;Park, Soo-Hong;Hwang, Cheol-Ju
    • Journal of KIISE:Information Networking
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    • v.36 no.1
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    • pp.50-56
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    • 2009
  • This paper proposes a new algorithm of clustering similar nodes defined as nodes having similar characteristic values in pure P2P environment. To compare similarity between nodes, we introduce a fitness function whose return value depends only on the two nodes' characteristic values. The higher the return value is, the more similar the two nodes are. We propose a GORGFM algorithm newly in conjunction with the fitness function to recommend and exchange nodes' characteristic values for an interest group formation and maintenance. With the GORGFM algorithm, the interest groups are formed dynamically based on the similarity of users, and all nodes will highly satisfy with the information recommended and received from nodes of the interest group. To evaluate of performance of the GORGFM algorithm, we simulated a matching rate by the total number of nodes of network and the number of iterations of the algorithm to find similar nodes accurately. The result shows that the matching rate is highly accurate. The GORGFM algorithm proposed in this paper is highly flexible to be applied for any searching system on the web.

Feature matching toy Omnidirectional Image based on Singular Value Decomposition

  • Kim, Do-Yoon;Lee, Young-Jin;Myung jin Chung
    • 제어로봇시스템학회:학술대회논문집
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    • 2002.10a
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    • pp.98.2-98
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    • 2002
  • $\textbullet$ Omnidirectional feature matching $\textbullet$ SVD-based matching algorithm $\textbullet$ Using SSD instead of the zero-mean correlation $\textbullet$ The similarity with the Gaussian weighted $\textbullet$ Low computational cost $\textbullet$ It describes the similarity of the matched pairs in omnidirectional images.

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Clustering of Stereo Matching Data for Vehicle Segmentation (차량분리를 위한 스테레오매칭 데이터의 클러스터링)

  • Lee, Ki-Yong;Lee, Joon-Woong
    • Journal of Institute of Control, Robotics and Systems
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    • v.16 no.8
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    • pp.744-750
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    • 2010
  • To segment instances of vehicle classes in a sparse stereo-matching data set, this paper presents an algorithm for clustering based on DP (Dynamic Programming). The algorithm is agglomerative: it begins with each element in the set as a separate cluster and merges them into successively larger clusters according to similarity of two clusters. Here, similarity is formulated as a cost function of DP. The proposed algorithm is proven to be effective by experiments performed on various images acquired by a moving vehicle.