• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.707-709
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• 2003

본 논문에서는 유사도(similarity) 기반 이미지 클러스터링 기법에 대하여 논하고자 한다. 비트맵 이미지의 특징을 추출하고 이러한 특징에 기반한 유사도 측정 함수들을 소개하고 이미지 클러스터링 알고리즘과 구현을 통한 실험 예제들에 대해서 설명한다. 이 실험에서 우리는 유사도에 따라 이미지들이 계층적(Hierarchical)으로 집단화 되는 계층적 클러스터링 알고리즘을 사용하였다. 이미지의 특징 표현을 위해서는 HSV 기반의 히스토그램을 이용하였다. 본 논문에서 제안한 기법의 실험 결과는 이미지 데이터베이스에서 유사한 이미지를 검색하는데 높은 효율성이 있는 것을 보여준다.

• Journal of KIISE:Databases
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• v.29 no.5
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• pp.358-366
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• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the high speed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space and generates a topological feature map. A topological feature map preserves the mutual relations (similarities) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Therefore each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented a k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• Journal of the Korea Computer Industry Society
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• v.7 no.5
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• pp.537-544
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• 2006

Image Search is one of the most exciting and fast growing research areas in the filed of multimedia technology. This paper conducts an empirical evaluation of color descriptor that uses the information of color distribution in color images, which is the most basic element for image search. With the experimental results, we observe that in the top 10% of precision, HSV, Daubechies 9/7 and 2 level decomposition have little better than others. Also histogram quadratic metrics outperform the Minkowski form distance metrics in similarity measurements, but spend more than 20 in computational times.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.2931-2946
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• 1997

In multimedia information retrieval applications, content-based image retrieval is essential for retrieving relevant multimedia documents. The purpose of our paper is to provide effective representation and efficient retrieval of images when a pixel-level original image is automatically or manually transformaed into its iconic image containing meaningful graphic descriptions, called icon objects. For this, we first propose new spatial match representationschemes to describe spatial relationships between icon objects accurately by expressing them as rectangles, rather than as points. In order to accelerate image searching, we also design an efficient retrieval method using a two-dimensional signature file organization. Finally, we show from our experiment that the proposed representation schemes achieve better retrieval effectiveness than the 9-DLT (Direction Lower Triangular) scheme.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.501-512
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• 2003

This paper proposes an indexing technique for fast retrieval of similar image subsequences using the multi-dimensional time warping distance. The time warping distance is a more suitable similarity measure than Lp distance in many applications where sequences may be of different lengths and/or different sampling rates. Our indexing scheme employs a disk-based suffix tree as an index structure and uses a lower-bound distance function to filter out dissimilar subsequences without false dismissals. It applies the normaliration for an easier control of relative weighting of feature dimensions and the discretization to compress the index tree. Experiments on medical and synthetic image sequences verify that the proposed method significantly outperforms the naive method and scales well in a large volume of image sequence databases.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.3
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• pp.171-180
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• 2003

A content-based image retrieval(CBIR) system builds the image database using low-level features such as color, shape and texture and provides similar images that user wants to retrieve when the retrieval request occurs. What the user is interest in is a response time in consideration of the building time to build the index database and the response time to obtain the retrieval results from the query image. In a content-based image retrieval system, the similarity computing time comparing a query with images in database takes the most time in whole response time. In this paper, we propose the two-phase search method with the clustering technique of feature vector in order to minimize the similarity computing time. Experimental results show that this two-phase search method is 2-times faster than the conventional full-search method using original features of ail images in image database, while maintaining the same retrieval relevance as the conventional full-search method. And the proposed method is more effective as the number of images increases.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10a
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• pp.127-129
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• 2000

일반적인 색 정보추출방법으로 색 히스토그램(Color Histogram)은 색의 분포나 응집성, 질감에 대한 구분능력이 없다는 단점을 가지고 있어 정환한 이미지 유사성 비교를 위해 추가적인 정보를 요구한다. Androutsos등은 Haar Wavelet 변환을 통해 이미지의 방향성 질감정보를 구하였다[1]. 하지만 이 방법은 Haar Wavelet 변환의 특성으로 인해 정확한 방향성 정보를 얻을 수 없었다. 본 논문에서는 인접 픽셀(pixel)값의 편차(deviaiton)를 이용하여 방향성 정보를 추출 성능을 향상시키는 방법을 제안하였고, Brodatz 112 질감 이미지와 실재 자연사진을 통해 방향성 질감의 성능을 평가하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.10a
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• pp.695-698
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• 2000

본 논문에서는 이미지 데이터 컬러 속성을 기반으로 한 영상 검색 방법을 제안한다. 두 이미지 사이의 유사성을 측정하기 위하여 컬러 히스토그램의 분포 특성을 이미지 데이터베이스 영상과질의 영상에서 계산하여 유사도를 결정하도록 설계하였다. 두 영상의 유사도를 측정하기 위해 두영상의 R, G, B 히스토그램에 대해서 같은 값에 대한 빈도 수의 차를 거리로 측정한 후, 구해진 거리의 차를 비교한 방법과 히스토그램의 분포 곡선을 이루는 방정식을 구한 수 있도록 곡선 정합을 한 후에 두 영상의 컬러 특징 속성에 관한 특징 값의 추출을 위해서 다항식 보간법에 의한 방정식을 이용한 방법을 소개한다. 공간 데이터베이스 시스템에서 질의에 대한 효율적인 처리를 위해 R-Tree와 최대 점을 이용하여 영상을 검색한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2579-2585
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• 2010

This paper proposes a dynamic human pose tracking method using motion-based search strategy from an image sequence obtained from a monocular camera. The proposed method compares the image features between 3D human model projections and real input images. The method repeats the process until predefined criteria and then estimates 3D human pose that generates the best match. When searching for the best matching configuration with respect to the input image, the search region is determined from the estimated 2D image motion and then search is performed randomly for the body configuration conducted within that search region. As the 2D image motion is highly constrained, this significantly reduces the dimensionality of the feasible space. This strategy have two advantages: the motion estimation leads to an efficient allocation of the search space, and the pose estimation method is adaptive to various kinds of motion.

• Journal of KIISE:Databases
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• v.31 no.1
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• pp.13-22
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• 2004

With the proliferation of multimedia data, there is an increasing need to support the indexing and retrieval of high-dimensional image data. Although there have been many efforts, the performance of existing multidimensional indexing methods is not satisfactory in high dimensions. Thus the dimensionality reduction and the approximate solution methods were tried to deal with the so-called dimensionality curse. But these methods are inevitably accompanied by the loss of precision of query results. Therefore, recently, the vector approximation-based methods such as the VA- file and the LPC-file were developed to preserve the precision of query results. However, the performance of the vector approximation-based methods depend largely on the size of the approximation file and they lose the advantages of the multidimensional indexing methods that prune much search space. In this paper, we propose a new index structure called the GC-tree for efficient similarity search in image databases. The GC-tree is based on a special subspace partitioning strategy which is optimized for clustered high-dimensional images. It adaptively partitions the data space based on a density function and dynamically constructs an index structure. The resultant index structure adapts well to the strongly clustered distribution of high-dimensional images.