• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.1-14
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• 2011

While the circulation of images become active, various requirements to manage increasing database are raised. The content-based technology is one of methods to satisfy these requirements. The image is represented by feature vectors extracted by various methods in the content-based technology. The global feature method insures fast matching speed because the feature vector extracted by the global feature method is formed into a standard shape. The global feature extraction methods are classified into two categories, the spatial feature extraction and statistical feature extraction. And each group is divided by what kind of information is used, color feature or gray scale feature. In this paper, we introduce various global feature extraction technologies and compare their performance by accuracy, recall-precision graph, ANMRR, feature vector size and matching time. According to the experiments, the spatial features show good performance in non-geometrical modifications, and the extraction technologies that use color and histogram feature show the best performance.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.1
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• pp.77-85
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• 2006

In this paper, we propose a hierarchical classifier of object images using neural networks for content-based image classification. The images for classification are object images that can be divided into foreground and background. In the preprocessing step, we extract the object region and shape-based texture features extracted from wavelet transformed images. We group the image classes into clusters which have similar texture features using Principal Component Analysis(PCA) and K-means. The hierarchical classifier has five layes which combine the clusters. The hierarchical classifier consists of 59 neural network classifiers learned with the back propagation algorithm. Among the various texture features, the diagonal moment was the most effective. A test with 1000 training data and 1000 test data composed of 10 images from each of 100 classes shows classification rates of 81.5% and 75.1% correct, respectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.119-126
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• 2005

We propose a new content-based image retrieval using a representative color histogram and directional pattern histogram that is adaptive to the classification characteristics of the image blocks. In the proposed method the color and pattern feature vectors are extracted according to the characteristics o: the block classification after dividing the image into blocks with a fixed size. First, the divided blocks are classified as either luminance or color blocks depending on the saturation of the block. Thereafter, the color feature vectors are extracted by calculating histograms of the block average luminance co-occurrence for the luminance block and the block average colors for the color blocks. In addition, block directional pattern feature vectors are extracted by calculating histograms after performing the directional gradient classification of the luminance. Experimental results show that the proposed method can outperform the conventional methods as regards the precision and the size of the feature vector dimension.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.5
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• pp.628-638
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• 1999

In this paper, we presents a method of retrieving 24 bpp RGB images based on color-spatial features. For each image, it is subdivided into regions by using similarity of color after converting RGB color space to CIE L*u*v* color space that is perceptually uniform. Our segmentation algorithm constrains the size of region because a small region is discardable and a large region is difficult to extract spatial feature. For each region, averaging color and center of region are extracted to construct color-spatial features. During the image retrieval process, the color and spatial features of query are compared with those of the database images using our similarity measure to determine the set of candidate images to be retrieved. We implement a content-based color image retrieval system using the proposed method. The system is able to retrieve images by user graphic or example image query. Experimental results show that Recall/Precision is 0.80/0.84.

• Broadcasting and Media Magazine
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• v.8 no.2
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• pp.149-159
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• 2003

현재 사회 전반에 걸쳐 급격히 증가하고 있는 멀티미디어 정보를 효율적으로 관리, 활용할 수 있는 방법이 다양하게 연구되고 있다. 본 연구에서는 내용기반 영상검색을 위한 다중 영상특징 추출방법과 특징결합 방법을 제시한다. 우선 전처리 및 캐니 에지 검출법으로 질의영상내 물체영역의 에지를 검출한다. 그 다음에 제안한 볼록 다각형 알고리즘을 통해 분할된 물체영상을 획득한다. 분할된 물체영상은 HSV 공간으로 변환되고 히스토그램 인터섹션 방법으로 유사도가 측정된다. 또한 분할된 물체영상은 웨블릿 변환 영상으로도 변환된다. 이러한 변환후 웨블릿 부밴드의 LL 영역에 제안하는 거리 밴드 평균 오토코릴로그램 알고리즘을 적용하여 오토코릴로그램 유사도를 측정한다. 그리고 GLCM을 이용한 엔트로피와 콘트라스트 유사도는 LH, HL 영역에서 측정된다. 전 과정을 통해 얻은 4개의 다중 영상특징은 수정된 보다 카운트 방법으로 결합되고 최종 유사도가 결정된다. 실험결과 제안한 다중 영상특징을 사용한 검색 방법이 단일 영상특징을 사용하는 검색 방법보다 소환성과 정확성의 성능에 있어 우수함을 보였다. 그리고 NMRR 측정에서도 개선된 성능을 보였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.39-42
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• 2011

본 논문에서는 영상들의 특징들을 추출하여 특징 값들의 비교를 통하여 질의 영상의 유사 영상을 검색하는 방법을 제안한다. 제안하는 방법은 입력 영상들의 색상 히스토그램으로 색상 특징 값들을 추출하고 질감 정보인 에지 정보와 이웃화소간의 공간 관계를 분석하여 질감 특징 값들을 추출하여 저장한 후 질의 이미지의 색상과 질감 특징들을 구하여 비교를 통하여 유사도를 분석하고 결과 영상을 보여준다. 또한 색상과 질감을 혼합하여 사용할 때 적응적으로 가중치를 부여함으로써 가중치가 적합하지 않아 발생하는 오 검출될 현상을 피할 수 있게 되었다. 실험을 통하여 기존의 방법과의 성능을 비교분석하였고 본 방법의 우수성을 입증하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.355-357
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• 2001

본 논문은 MPEG-7을 기반으로 내용기반 검색을 위한 자동화 시스템을 구현하고자 한다. 하위레벨 특징(Low-level feature) 추출에서 DDL(Description Definition Language) 작성까지 자동화 시스템을 설계 및 구현하고, 프로듀서의 입장에서 고려된 고정적인 DS(Fixed Description Scheme)에 대응하는 유동적인 DS(Dynamic Description Scheme)를 이용한 사용자 중심의 개인적인 비디오 검색 시스템 구현을 목적으로 한다.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.36-38
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• 1999

본 논문에서는 대용량의 XML 문서를 효과적으로 저장, 관리 및 구조 기반 검색이 가능한 XML 저장관리시스템을 설계하고 구현한다. 구현한 XML 저장관리시스템은 관계형 모델을 기반으로 하고, XML 문서 전체를 저장하는 비분할 저장 모델을 사용하며, DTD에 따라 스키마가 생성되는 동적 스키마 생성 모델을 특징으로 한다. 본 논문의 XML 저장관리 시스템은 BRS 검색엔진과 ORACLE을 기반으로 하며 질의처리기 및 검색결과생성기, XML 객체관리자, XML 인덱스관리자, 구조검색엔진 등으로 구성된다. 이를 통하여 내용 및 애트리뷰트 검색 뿐만 아니라 다양한 구조 정보검색을 효율적으로 지원한다.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.705-710
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• 2003

Recent years, as a rapid development of multimedia technology, video database system to retrieve video data efficiently seems to core technology in the oriented society. This thesis describes an efficient automatic frame detection and location method for content based retrieval of video. Frame extraction part is consist of incoming / outgoing car frame extraction and car number frame extraction stage. We gain star/end time of car video also car number frames. Frames are selected at fixed time interval from video and key frames are selected by color scale histogram and edge operation method. Car frame recognized can be searched by content based retrieval method.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.181-185
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• 2021

As part of an effort to derive measures to prevent school violence, which is continuously emphasized in the school field, this study tried to examine the topic that has recently become an issue related to school violence from the perspective of data science. In particular, it was attempted to crawl posts related to school violence using online SNS data and examine the characteristics of each type by using the topic modeling method. As a result of arranging the keywords for each topic derived from the topic modeling analysis by type, it was possible to divide the contents into three main categories: prevention of school violence, punishment of perpetrators, and measures to be taken. First, as the contents of school violence prevention activities, it is the contents of the role of specialized organizations for the prevention of school violence. Second, it was derived from the contents of measures and procedures for school violence. Third, it was possible to examine the contents of recent issues of school violence. In future research, it is necessary to conduct research that is used to solve the social problems facing based on data-based prediction.