KR100318512B1 - How to calculate similarity between two groups - Google Patents
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- KR100318512B1 KR100318512B1 KR1019980022057A KR19980022057A KR100318512B1 KR 100318512 B1 KR100318512 B1 KR 100318512B1 KR 1019980022057 A KR1019980022057 A KR 1019980022057A KR 19980022057 A KR19980022057 A KR 19980022057A KR 100318512 B1 KR100318512 B1 KR 100318512B1
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Abstract
본 발명은 두 그룹내의 클러스터들이 얼마나 잘 병합되는지를 파악하여 두 그룹간의 유사성을 인식하도록 된 두 그룹간의 유사도 계산 방법을 제공하기 위한 것이다.The present invention is to provide a method for calculating the similarity between two groups to grasp how well the clusters in the two groups are merged to recognize the similarity between the two groups.
이를 위해 본 발명은, 이미 구해진 다수의 클러스터로 된 두 그룹내의 클러스터중에서 일정 조건을 만족하는 두개의 클러스터를 선택하고, 선택된 두개의 클러스터 사이의 유사도를 계산한 후 선택된 두개의 클러스터를 병합하고서 상기 클러스터 선택과정으로 복귀하여 선택할 클러스터들이 없을 때까지 상기 과정들을 반복적으로 수행함으로써, 보다 신빙성있는 유사도 계산이 되고 실제적으로도 매우 우수한 결과를 보여준다.To this end, the present invention selects two clusters satisfying a predetermined condition among clusters in two groups of a plurality of clusters already obtained, calculates the similarity between the two selected clusters, and then merges the two selected clusters. By repeating the above steps until there are no clusters to choose from returning to the selection process, a more reliable similarity calculation is obtained and in practice shows very good results.
Description
본 발명은 유사도 계산 방법에 관한 것으로, 보다 상세하게는 비교해야 할 두개의 자료가 각각 서로 다른 개수의 클러스터로 이루어져 있을 때 이들의 유사도를 가장 적절하게 계산해 내도록 한 두 그룹간의 유사도 계산 방법에 관한 것이다.The present invention relates to a method of calculating similarity, and more particularly, to a method of calculating similarity between two groups in which two data to be compared are composed of different numbers of clusters, so as to calculate the similarity thereof most appropriately. .
종래의 유사도 비교 방법은 단순히 가장 거리가 가까운 클러스터의 중심값의차와 그 분포비율을 곱해서 더하는 단순한 방법으로 이루어져 있다. 이것은 계산의 용이함을 주기는 하지만, 그 결과가 만족스럽지 못한 문제가 있다.The conventional similarity comparison method simply consists of multiplying the difference of the center values of the clusters closest to the distribution ratio and adding them. Although this gives ease of calculation, there is a problem that the result is not satisfactory.
또한, 운송비용 절감문제(Transportation Problem)로 이러한 두 그룹의 유사도를 계산하려는 시도가 있었는데, 이것은 문제 자체가 유사도를 계산하는 것이 아닌 다른 문제로 전환하여 푸는 것으로, 그 결과가 실험적으로는 인정이 되지만 실제 유사성을 계산해 준다는 근거가 없다는 문제가 있다.In addition, there have been attempts to calculate the similarity of these two groups as a transportation problem, which is solved by converting the problem into a different problem rather than calculating the similarity. The problem is that there is no basis for calculating the actual similarity.
따라서 본 발명은 상기한 종래의 문제점을 해결하기 위해 이루어진 것으로, 두 그룹내의 클러스터들이 얼마나 잘 병합되는지를 파악하여 두 그룹간의 유사성을 인식하도록 된 두 그룹간의 유사도 계산 방법을 제공함에 목적이 있다.Accordingly, an object of the present invention is to provide a method for calculating similarity between two groups in which the clusters in the two groups are recognized so as to recognize the similarity between the two groups.
상기한 목적을 달성하기 위해 본 발명의 바람직한 실시예에 따른 두 그룹간의 유사도 계산 방법은, 이미 구해진 다수의 클러스터로 된 두 그룹내의 클러스터중에서 일정 조건을 만족하는 두개의 클러스터를 선택하는 과정과, 상기 선택된 두개의 클러스터 사이의 유사도를 계산하는 과정 및, 상기 선택된 두개의 클러스터를 병합하고서 상기 클러스터 선택과정으로 복귀하여 선택할 클러스터들이 없을 때까지 상기 과정들을 반복적으로 수행하는 과정을 구비한다.In order to achieve the above object, a method for calculating the similarity between two groups according to a preferred embodiment of the present invention includes the steps of selecting two clusters satisfying a predetermined condition among clusters in two groups having a plurality of clusters already obtained; Calculating similarity between the two selected clusters, merging the two selected clusters, and returning to the cluster selection process, and repeatedly performing the above processes until there are no clusters to select.
도 1은 본 발명의 실시예에 채용되는 유사도 계산장치의 블럭구성도,1 is a block diagram of a similarity calculation device employed in an embodiment of the present invention;
도 2는 본 발명의 실시예에 따른 유사도 계산 방법을 설명하는 플로우차트이다.2 is a flowchart illustrating a similarity calculation method according to an embodiment of the present invention.
< 도면의 주요부분에 대한 부호의 설명><Description of the reference numerals for the main parts of the drawings>
10 : 데이터베이스 입력기 20 : 특징 추출기10: database inputter 20: feature extractor
25 : 특징벡터 디렉토리 30 : 유사도 계산기25: Feature Vector Directory 30: Similarity Calculator
40 : 영상 디렉토리 50 : 소형 영상 생성기40: Image Directory 50: Small Image Generator
60 : 소형 영상 디렉토리 70 : 인덱스60: small image directory 70: index
80 : 질의도구 90 : 결과 뷰어80: Query Tool 90: Result Viewer
이하, 본 발명의 실시예에 대해 첨부된 도면을 참조하여 보다 상세히 설명한다.Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
도 1은 본 발명의 실시예에 채용되는 유사도 계산장치의 블럭구성도이다.1 is a block diagram of a similarity calculating device employed in an embodiment of the present invention.
데이터베이스(DB) 입력기(10)는 영상을 데이터베이스화하기 위한 모든 과정을 자동으로 처리해 주는데, 처리해야 할 영상이 영상 디렉토리(40)에 이미 입력되어 있는지를 자동으로 판단한 후 입력되어 있지 않으면 소형 영상 생성기(50)를 구동시켜 새로운 소형 영상(thumbnail; 화면표시를 용이하게 하기 위해 큰 영상을 100×100정도의 작은 영상으로 만든 것)을 만들어 소형 영상 디렉토리(60)에 저장시킨다. 아울러, 새로운 영상에 대한 특징벡터가 없으므로 특징 추출기(20)를 구동시켜 새로운 영상의 특징을 추출하여 특징벡터를 만들고, 그 특징벡터를 특징벡터 디렉토리(25)에 저장시킨다.The database inputter 10 automatically handles all the processes for making the database into a database, and automatically determines whether the image to be processed is already input to the image directory 40, and if not, the small image generator. By driving the 50, a new thumbnail (thumbnail) is made of a small image of about 100 x 100 in order to facilitate display, and is stored in the small image directory 60. In addition, since there is no feature vector for the new image, the feature extractor 20 is driven to extract a feature of the new image to create a feature vector, and the feature vector is stored in the feature vector directory 25.
그리고, 상기 데이터베이스 입력기(10)는 기존에 동일한 영상이 상기 영상 디렉토리(40)에 존재한다면 그 영상에 대해서는 더 이상 처리하지 않고 후속 영상을 선택하여 상기의 동작을 반복시킨다.If the same image exists in the image directory 40, the database input unit 10 selects a subsequent image without further processing the image and repeats the above operation.
한편, 상기 특징 추출기(20)는 본 발명에서 상정하는 여러 클러스터로 이루어진 두개의 그룹을 추출한다.On the other hand, the feature extractor 20 extracts two groups consisting of several clusters assumed in the present invention.
유사도 계산기(30)는 상기 특징 추출기(20)에 의해 추출된 일정 개수의 특징벡터를 이용하여 영상 디렉토리(40)내의 다른 영상의 특징벡터와의 유사도를 계산할 뿐만 아니라, 상기 특징 추출기(20)에 의해 추출된 두 그룹내의 클러스터중에서 임의의 임계치 내의 거리에 있는 클러스터를 선택하여 유사도를 계산하고 상기 선택된 클러스터를 병합하며, 그 계산된 유사도를 테이블로 만들어 인덱스(70)를 생성한다. 상기 유사도 계산기(30)에서 인덱스(70)를 생성하게 되면 영상의 데이터베이스화 작업은 종료된다.The similarity calculator 30 not only calculates the similarity with the feature vectors of other images in the image directory 40 using a predetermined number of feature vectors extracted by the feature extractor 20, but also calculates the similarity with the feature extractor 20. From among the clusters in the two groups extracted by selecting a cluster at a distance within an arbitrary threshold to calculate similarity and merging the selected clusters, the calculated similarity is made into a table to generate an index 70. When the index 70 is generated by the similarity calculator 30, the database operation of the image is completed.
질의도구(80)는 일반 사용자가 기입력된 영상 디렉토리(40)에서 영상을 검색하고자 할 때 사용하는 것으로서, 이 질의도구(80)는 사용자의 검색요구에 맞게 질의어를 분석한 후 상기 특징 추출기(20)와 유사도 계산기(30)를 이용하여 인덱스 (70)로부터 검색된 영상을 결과 뷰어(90)로 보내준다.The query tool 80 is used when a general user wants to search for an image in a pre-entered image directory 40. The query tool 80 analyzes a query word according to a user's search request and then extracts the feature extractor ( 20) and transmits the image retrieved from the index 70 to the result viewer 90 by using the similarity calculator 30.
상기 결과 뷰어(90)는 상기 인덱스(70)로부터 검색되어 입력되는 영상을 받아 상기 소형 영상 디렉토리(60)에서 소형 영상을 가져와 검색 사용자에게 보여준다.The result viewer 90 receives the image searched from the index 70 and inputs the received image and displays the small image from the small image directory 60 to the search user.
이어, 본 발명의 실시예에 따른 유사도 계산 방법에 대해 도 2의 플로우차트를 참조하여 설명하면 다음과 같다.Next, a similarity calculation method according to an embodiment of the present invention will be described with reference to the flowchart of FIG. 2.
일단, 유사도 계산기(30)는 유사도 변수를 0으로 초기화시키고서(단계 101), 상기 특징 추출기(20)에 의해 추출된 두 그룹내의 클러스터중에서 다음의 수학식 1을 만족하는 임계치 내의 거리에 있는 클러스터를 선택하게 된다(단계 102).First, the similarity calculator 30 initializes the similarity variable to zero (step 101), and the cluster at a distance within a threshold that satisfies the following equation 1 among the clusters in the two groups extracted by the feature extractor 20. (Step 102).
[수학식 1][Equation 1]
C〈 TC <T
여기서,, T는 임의로 선택된 임계값이고,here, , T is a randomly chosen threshold,
로서 두 클러스터의 중심간의 거리이다. This is the distance between the centers of two clusters.
만약, 선택하려는 클러스터가 존재하지 않을 경우(단계 103에서 "YES")에는 종료하게 되는 반면에, 선택하려는 클러스터가 존재하는 경우에는 단계 104로 전환되어 선택된 두 클러스터간의 유사도를 계산하여 초기화되어 있던 유사도 변수에 계산된 유사도를 더하게 된다. 상기 선택된 두 클러스터간의 유사도 계산은 다음의 수학식 2에 의해 행해진다.If the cluster to be selected does not exist (“YES” in step 103), the process is terminated. On the other hand, if the cluster to be selected exists, the process returns to step 104 to calculate the similarity between the two selected clusters. The calculated similarity is added to the variable. The similarity calculation between the two selected clusters is performed by the following equation.
[수학식 2][Equation 2]
여기서는 두 클러스터들간의 유사도이고, IA= {xA,yA,zA}는 클러스터 A의 대표색상, PA는 IA의 색상 비율, σ2 A는 대표색상 IA에 대한 분산, IB= {xB,yB,zB}는 클러스터 B의 대표색상, PB는 IB의 색상 비율, σ2 B는 대표색상 IB에 대한 분산, σ2 k는 클러스터가 병합된 경우의 분산이다. 그리고, 상기 분산(σ2 k)은 계산량의 감소를 위해 전체 영상 데이터베이스내의 모든 영상에 대해 계산한 분산의 평균값으로 대치할 수 있다.here Is the similarity between the two clusters, I A = {x A , y A , z A } is the representative color of cluster A, P A is the color ratio of I A , σ 2 A is the variance of the representative color I A , I B = {x B , y B , z B } is the representative color of cluster B, P B is the color ratio of I B , σ 2 B is the variance of the representative color I B , and σ 2 k is the case of clusters merged. Dispersion. The variance σ 2 k may be replaced with an average value of variances calculated for all images in the entire image database in order to reduce the amount of calculation.
이후, 단계 105에서는 선택된 두 클러스터를 병합하여 하나의 클러스터로 만들고, 다시 상기 단계 102로 복귀하여 상술한 동작을 반복하게 된다.Thereafter, in step 105, the two selected clusters are merged into one cluster, and the process returns to step 102 to repeat the above-described operation.
만약, 단계 102에서 조건에 맞는 선택된 클러스터가 없다면 단계 103에서 종료하게 되고, 이로써 모든 유사도 계산동작은 마치게 된다. 그 결과 유사도 변수에는 전체 두 그룹간의 유사도 값이 저장된다.If there is no cluster selected that meets the conditions in step 102, the process ends in step 103, thereby completing all similarity calculation operations. As a result, the similarity variable stores the similarity value between the two groups.
이상 설명한 바와 같은 본 발명에 의하면, 서로 다른 개수의 클러스터로 구성된 두 그룹간의 유사도를 각 그룹간의 클러스터들이 얼마나 잘 병합되는지의 계산에 의해 파악함으로써, 보다 신빙성있는 유사도 계산이 되고 실제적으로도 매우 우수한 결과를 보여준다.According to the present invention as described above, the similarity between the two groups composed of different number of clusters by grasping how well the clusters between the groups are merged, the more reliable similarity calculation and practically very good results Shows.
한편 본 발명은 상술한 실시예로만 한정되는 것이 아니라 본 발명의 요지를 벗어나지 않는 범위내에서 수정 및 변형하여 실시할 수 있다.On the other hand, the present invention is not limited only to the above-described embodiments, but may be modified and modified without departing from the scope of the present invention.
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| KR20000072192A (en) * | 2000-08-16 | 2000-12-05 | 박성용 | The method of the service and system of automatic statistical report of music brocating |
| KR100784965B1 (en) * | 2001-03-27 | 2007-12-11 | 삼성테크윈 주식회사 | Real imager that can search shooting video |
| KR20030026792A (en) * | 2001-09-27 | 2003-04-03 | 홍종국 | Document Storage System with specific marking tool |
| US7831832B2 (en) | 2004-01-06 | 2010-11-09 | Microsoft Corporation | Digital goods representation based upon matrix invariances |
| US20090187588A1 (en) * | 2008-01-23 | 2009-07-23 | Microsoft Corporation | Distributed indexing of file content |
| CN102156707A (en) * | 2011-02-01 | 2011-08-17 | 刘中华 | Video abstract forming and searching method and system |
| CN103927545B (en) * | 2014-03-14 | 2017-10-17 | 小米科技有限责任公司 | Clustering method and relevant apparatus |
| KR20220039075A (en) * | 2020-09-21 | 2022-03-29 | 삼성전자주식회사 | Electronic device, contents searching system and searching method thereof |
| US12099537B2 (en) | 2020-09-21 | 2024-09-24 | Samsung Electronics Co., Ltd. | Electronic device, contents searching system and searching method thereof |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH06176072A (en) * | 1992-12-08 | 1994-06-24 | Toshiba Corp | Similarity retrieving device |
-
1998
- 1998-06-12 KR KR1019980022056A patent/KR100312331B1/en not_active Expired - Fee Related
- 1998-06-12 KR KR1019980022057A patent/KR100318512B1/en not_active Expired - Fee Related
- 1998-06-12 KR KR1019980022058A patent/KR100302366B1/en not_active Expired - Lifetime
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH06176072A (en) * | 1992-12-08 | 1994-06-24 | Toshiba Corp | Similarity retrieving device |
Also Published As
| Publication number | Publication date |
|---|---|
| KR100312331B1 (en) | 2001-12-28 |
| KR19990071353A (en) | 1999-09-27 |
| KR100302366B1 (en) | 2001-11-30 |
| KR19990071354A (en) | 1999-09-27 |
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