• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.10B
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• pp.1902-1911
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• 1999

In this paper we present a content-based image retrieval algorithm using the visual feature vectors which describe the spatial characteristics of objects. The proposed technique uses the Gaussian mixture model(GMM) to represent multi-colored objects and the expectation maximization(EM) algorithm is employed to estimate the maximum likelihood(ML) parameters of the model. After image segmentation is performed based on GMM, the shape and color features are extracted from each object using Fourier descriptors and color histograms, respectively. Image retrieval consists of two steps: first, the shape-based query is carried out to find the candidate images whose objects have the similar shapes with the query image and second, the color-based query is followed. The experimental results show that the proposed algorithm is effective in image retrieving by using the spatial and visual features of segmented objects.

• Journal of Integrative Natural Science
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• v.12 no.2
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• pp.35-43
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• 2019

In the content-based image search properties, form information is simple because only the contours of objects are available, and although it can effectively extract the characteristics of the objects, it is sensitive to external noise. The radial distortion, one of these noises, is most prominent in the eyewear and, due to the structural characteristics of the imaging equipment, radiative distortion occurs in almost all imaging equipment. It is very important to determine the similarity of the objects in the images in which these distortions occurred to the actual objects. In order to improve this problem, we propose a strong image search technique for formative noise and radiative distortion using regularization phase angles and moments. Through simulation using Wang DB, the proposed algorithm proved excellent performance for radiation distortion that occurs in general. In addition, a system optimized for database can be implemented by making appropriate changes to the threshold values, enabling image retrieval with the desired level of confidence in various systems. The algorithm proposed in this paper is expected to be utilized as an optimal imaging system by extracting morphological form information of multimedia data.

• Journal of Platform Technology
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• v.6 no.4
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• pp.26-33
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• 2018

In this paper, we propose an image retrieval algorithm for real-time processing and design it as hardware. The proposed method is based on the classification of BoWs(Bag of Words) algorithm and proposes an image search algorithm using bit stream. K-fold cross validation is used for the verification of the algorithm. Data is classified into seven classes, each class has seven images and a total of 49 images are tested. The test has two kinds of accuracy measurement and speed measurement. The accuracy of the image classification was 86.2% for the BoWs algorithm and 83.7% the proposed hardware-accelerated software implementation algorithm, and the BoWs algorithm was 2.5% higher. The image retrieval processing speed of BoWs is 7.89s and our algorithm is 1.55s. Our algorithm is 5.09 times faster than BoWs algorithm. The algorithm is largely divided into software and hardware parts. In the software structure, C-language is used. The Scale Invariant Feature Transform algorithm is used to extract feature points that are invariant to size and rotation from the image. Bit streams are generated from the extracted feature point. In the hardware architecture, the proposed image retrieval algorithm is written in Verilog HDL and designed and verified by FPGA and Design Compiler. The generated bit streams are stored, the clustering step is performed, and a searcher image databases or an input image databases are generated and matched. Using the proposed algorithm, we can improve convenience and satisfaction of the user in terms of speed if we search using database matching method which represents each object.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.40
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• pp.81-90
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• 1996

It is one of the most important thing to select pneumatic components in pneumatic system design. For the purpose of selecting pneumatic components, case objects are described as a knowledge representation and the most similar case object must be selected by decision making in computer. In this paper, case objects are represented using the methodology that is used for CBR(Case Base Reasoning) and methodology that the most similar case can be selected is Proposed. Algorithm VIWNNR(Varying Index Weight-based Nearer Neighbor Retrieval) is accomplished by varying index weight, that is not considering a index matching as true or false but varying a size of weight according to the degree of matching and enhance the flexibility of SCRM(Similar Case Retrieval Module) involving fuzzy concept in matching the cases. SCRM is tested In verify the feasibility to select pneumatic linear components and is peformed effectively.

• Korean Journal of Cognitive Science
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• v.2 no.1
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• pp.193-204
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• 1990

This paper discusses a reasoning model of information retrieval with a hierarchical thesaurus.The model computes the conceptual distance between a query and an object,both are indexed with weighted terms from a hierarchical thesaurus. The proposed model allows Boolean operators for user queries and edge weights for a hierarchical thesaurus. Experimental results have shown that the proposed model simulates, with surprising accuracy, people in the assessment of conceptual closeness between queries objects.

• Journal of Integrative Natural Science
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• v.12 no.4
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• pp.122-126
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• 2019

In this paper, we propose a simple and efficient retrieval technique using the feature value of the corner region, which is one of the shape information attributes of images. The proposed algorithm extracts the edges and corner points of the image and rearranges the feature values of the corner regions doubly, and then measures the similarity with the image in the database using the correlation of these feature values as the feature vector. The proposed algorithm is confirmed to be more robust to rotation and size change than the conventional image retrieval method using the corner point.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.7
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• pp.11-22
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• 1992

In this paper, a new indexing technique is proposed for efficient evaluation of nested queries on aggregation hierarchy in object-oriented data model. As an index data structure, an extended $B^{+}$ tree is introduced in which instance identifier to be searched and path information used for update of index record are stored in leaf node and subleaf node, respectively. the retrieval and update algorithm on the introduced index data structure is provided. Comparisons under a variety of conditions are given with current indexing techniques, showing improved performance in cost, i.e., the total number of pages accessed for retrieval and update.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.500-502
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• 1998

최근 정보 사회에서 중요한 기술로 자리잡은 멀티미디어 정보 검색에 대한 다양한연구가 진행 중에 있다. 본 논문은 정지 화상에 대한 CBIR(Content-Based Image Retrieval)방법 중 칼라 정보를 이용한 방법에서 공간 정보를 충분하게 표현할 수 있는 알고리즘을 제안한다. 일반적으로 칼라 정보를 이용한 CBIR에서는 공간정보를 표현하기 위하여 인위적으로 영상을 여러 개로 분할하는 방법이나 영상의 히스토그램 내에서 영상의 위치 정보를 이용하는 방법 등이 연구되었다. 본 논문에서는 기존의 방법을 칼라 오브젝트의 추출 방법에 따라 1차와 2차 관계에 의한 방법으로 분류하고, 이동, 회전 특히 크기 변화(축소, 확대)에 탁월한 성능을 보이는 3차 칼라 오브젝트 관계를 이용한 방법을 소개한다. 제안된 알고리즘은 주어진 영상으로부터 양자화 된 24개의 버킷(bucket)을 생성해서 각 버킷 내의 칼라에 대한 색의 표준 편차로 색의 분산 정도를 나타내고, 빈도수가 높은 3개 버킷의 평균 칼라 위치를 계산해서 그들의 상호 각도를 추출하여 영상의 특징 벡터로 사용하였다. 실험결과 기존 방법보다 특히 영상의 크기 변화에 대해 좋은 결과를 얻을 수 있었으며, 계산량도 적어 효율적임을 보여 주었다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.8
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• pp.1861-1873
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• 1996

This paper describes the content-based image retrieval system that is implemented to retrieve images using constituent rate of lines and Bezier curves. We proposed the line and Bezier curve extraction algorithm which extracts lines and curve that are fitted on the contour information of images. For this extration, it was necessary to remove internal area of the proprocessed object within images and to approximate its contour to polygon, and proposed retrevial algorithm which gets the simularity using the consitituent rate of lines and curves and perform the simularity matching.

• Proceedings of the KAIS Fall Conference
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• 2000.10a
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• pp.208-213
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• 2000

본 논문은 정지 화상에 대한 CBIR(Content-Based Image Retrieval)방법 중 칼라 특성을 이용해서 영상 내 공간 정보를 충분하게 표현할 수 있는 알고리즘을 제안한다. 일반적으로 칼라 특성을 이용한 CBIR은 영상 내 공간정보를 충분하게 표현하지 못하는 단점을 지니고 있다. 이에 기존 논문에서는 인위적으로 영상을 여러 개로 분할하는 방법 등으로 공간정보를 표현하고자 하였지만 특징벡터의 수가 급격히 늘어남에 따라 검색효율이 저하된다는 단점을 가지고있다. 본 논문에서는 기존의 방법을 칼라 오브젝트의 추출 방법에 따라 1차와 2차 관계에 의한 방법으로 분류하고, 이동, 회전 특히 크기 변화(축소, 확대)에 탁월한 성능을 보이는 칼라 오브젝트의 3차 관계를 이용한 방법을 소개한다. 주어진 영상으로부터 양자화된 24개의 버킷을 생성해서 각 버킷 내의 칼라에 대한 색의 표준 편차로 색의 분산 정도틀 나타내고, 히스토그램의 빈도수가 높은 세 개 버킷의 평균 칼라 위치를 계산해서 그들의 상호 각도를 추출하여 영상의 특징 벡터로 사용한을 제안하였다. 실험결과 기존 방법보다 특히 영상의 크기 변화에 대해 좋은 결과를 얻을 수 있었으며, 계산량도 적어 효율적임을 보여 주었다.