• Proceedings of the Korea Information Processing Society Conference
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• 2003.11b
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• pp.825-828
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• 2003

웹에는 막대한 양의 멀티미디어 데이터가 있다. 최근에 이러한 멀티미디어 데이터의 효과적 검색을 위한 많은 시스템이 개발되고 있으며, 그 결과 내용기반 이미지 검색과 같은 이미지의 내용을 인식하고 검색하고자 하는 연구가 진행되고 있다. 그렇지만 지금까지의 연구는 웹 검색엔진에 멀티미디어의 검색을 위한 또 하나의 검색엔진이 필요하여 그 무게가 커지고 그 성능도 저하된다. 이에 본 논문에서는 차세대 웹인 시맨틱 웹에서의 의미적 검색에 초점을 맞추어 멀티미디어 데이터 각각에 의미적 정보를 추가하여 웹 문서를 작성한다면 그 검색에 또 다른 검색엔진의 사용없이 효과적 검색이 이루어 질 것을 목적으로 하고 있다. 먼저, 간단한 이미지내에서 객체간의 공간적 관계를 표현하기 위한 새로운 Axiom 을 정의하고, 이를 적용하여 시맨틱 웹에서의 의미적 이미지 검객을 꾀한다.

• 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.

• The Transactions of the Korea Information Processing Society
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• v.2 no.6
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• pp.903-917
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• 1995

In most database systems images have been indexed indirectly using related texts such as captions, annotations and image attributes. But there has been an increasing requirement for the image database system supporting the storage and retrieval of images directly by content using the information contained in the images. There has been a few indexing methods based on contents. Among them, Pertains proposed an image indexing method considering spatial relationships and properties of objects forming the images. This is the expansion of the other studies based on '2-D string. But this method needs too much storage space and lacks flexibility. In this paper, we propose a more flexible index structure based on kd-tree using paging techniques. We show an example of extracting keys using normalization from the from the raw image. Simulation results show that our method improves in flexibility and needs much less storage space.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.823-828
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• 2003

The main objective of this paper is to provide a methodology of feature extraction using shape of image objects for content-based image retrieval. The shape of most real-life objects is irregular, and hence there is no universal approach to quantify the shape of an arbitrary object. In particular. electronic catalogs contain many image objects for their products. In this paper, we perform feature extraction based on individual objects in images rather than on the whole image itself, since our method uses a shape-based approach of objects using RLC lines within an image. Experiments show that shape parameters distinctly represented image objects and provided better classification and discrimination among image objects in an image database compared to Texture.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.15 no.2
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• pp.97-115
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• 2004

The paper examined the concepts and features of content-based Image and Video retrieval systems. It then analyzed the retrieval performance of on five content_based retrieval systems in terms of usability and retrieval features. The results showed that the combination of content_based retrieval techniques and meta-data based retrieval will be able to improve the retrieval effectiveness.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.579-582
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• 2019

이미지의 특정 객체를 제거할 때, 주변 환경을 고려하면서 제거하기에 어려움이 있다. 본 연구는 특정 객체가 제거되면서 생기는 빈자리를 자연스럽게 보완하기 위해서 이미지 내용을 기반으로 이미지를 변경하는 Seam Carving 알고리즘을 이용하여 보다 자연스러운 결과 이미지를 생성하는 프로그램을 구현했다.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.49-59
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• 2009

Contents-based image retrieval methods are in general more objective and effective than text-based image retrieval algorithms since they use color and texture in search and avoid annotating all images for search. SIM(Self-organizing Image browsing Map) is one of contents-based image retrieval algorithms that uses only browsable mapping results obtained by SOM(Self Organizing Map). However, SOM may have an error in selecting the right BMU in learning phase if there are similar nodes with distorted color information due to the intensity of light or objects' movements in the image. Such images may be mapped into other grouping nodes thus the search rate could be decreased by this effect. In this paper, we propose an improved SIM that uses HSV color model in extracting image features with color quantization. In order to avoid unexpected learning error mentioned above, our SOM consists of two layers. In learning phase, SOM layer 1 has the color feature vectors as input. After learning SOM Layer 1, the connection weights of this layer become the input of SOM Layer 2 and re-learning occurs. With this multi-layered SOM learning, we can avoid mapping errors among similar nodes of different color information. In search, we put the query image vector into SOM layer 2 and select nodes of SOM layer 1 that connects with chosen BMU of SOM layer 2. In experiment, we verified that the proposed SIM was better than the original SIM and avoid mapping error effectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.35-38
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• 2004

기존의 내용기반 이미지 검색 시스템은 색상, 질감, 모양등과 같은 특징 벡터를 추출하여 검색하는 방법이 많이 연구되어 왔다. 특히 색상 정보는 이미지를 검색하기 위하여 중요한 정보로 사용되고 있다. 따라서 색상 이미지를 검색하기 위해서 평균 RGB, HSI값을 이용하거나 히스토그램을 이용하는 방식이 많이 사용 되어왔다. 본 논문에서는 사람이 시각적으로 보고 느끼는 색상(H), 채도(S), 명도(I) 방식을 이용한 HSI값을 사용하여 색상 의미를 이용한 지능적 이미지 검색 기법을 제안하고 알고리즘을 설명한다. 색상 의미(Color Semantics)란 사람의 시각적인 특징을 기반으로 칼라 이미지에 적용하여 감성 형용사 기반으로 검색할 수 있는 방법이다. 색상 의미를 이용한 지능적 이미지 검색은 색상-기반 질의(color-based retrieval)를 제공할 뿐만 아니라 인간의 감성이나 느낌에 의한 의미-기반 질의(semantic-based retrieval)방식을 가능하게 한다. 즉, "시원한 이미지" 혹은 "부드러운 이미지"를 검색하는 방식이다. 따라서 사용자의 검색 의도를 보다 정확하게 표현할 수 있으며, 검색의 결과에 대한 만족도를 향상 시킬 수 있다.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.733-744
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• 1996

Content-based image retrieval(CBIR) is an image data retrieval methodology using characteristic values of image data those are generated by system automatically without any caption or text information. In this paper, we propose a content-based image data retrieval method using shape and color features of image data as characteristic values. For this, we present some image processing techniques used for feature extraction and indexing techniques based on trie and R tree for fast image data retrieval. In our approach, image query result is more reliable because both shape and color features are considered. Also, we how an image database which implemented according to our approaches and sample retrieval results which are selected by our system from 200 sample images, and an analysis about the result by considering the effect of characteristic values of shape and color.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.705-708
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• 2006

본 연구는 유방암 이미지와 가장 비슷한 이미지를 찾아내는 시스템을 내용기반 검색 기법을 이용하여 구축함으로써 사람의 눈으로만 진단하는 결론에 있어서 좀더 객관적인 데이터를 부여함을 목적으로 한다.