• 융합신호처리학회논문지
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• 제2권1호
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• pp.28-37
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• 2001

본 논문에서는 이진 영상의 2차원 히스토그램을 이용하여 추출한 형태 정보와 HSI 컬러 좌표를 이용한 색상 정보를 결합한 영상 검색 기법을 제안한다. 또한, 제안된 방식은 부분영상의 유사도 비교를 통한 영상의 위치 정보를 추출한다 이 검색 기법을 형태 정보와 색상 정보에 활용함으로써 이진 영상으로 비교가 힘든 영역 정보의 검색도 가능하게 한다 그 결과 기존의 색상기반 영상 검색 기법에 비해 제안된 기법은 Frecision/Recall로 표현된 정량적 결과에서 훨씬 우수한 성능을 보임을 실험으로 확인할 수 있다. 특히, 제안된 검색 기법은 영상의 회전이나 객체의 이동 등이 발생한 영상에 대해서도 우수한 검색 효율을 보인다.

• 한국측량학회지
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• 제30권6_2호
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• pp.665-672
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• 2012

Amid rapidly increasing imagery inputs and their volume in a remote sensing imagery database, Content-Based Image Retrieval (CBIR) is an effective tool to search for an image feature or image content of interest a user wants to retrieve. It seeks to capture salient features from a 'query' image, and then to locate other instances of image region having similar features elsewhere in the image database. For a CBIR approach that uses texture as a primary feature primitive, designing a texture descriptor to better represent image contents is a key to improve CBIR results. For this purpose, an extended feature vector combining the Gabor filter and co-occurrence histogram method is suggested and evaluated for quantitywise and qualitywise retrieval performance criterion. For the better CBIR performance, assessing similarity between high dimensional feature vectors is also a challenging issue. Therefore a number of distance metrics (i.e. L1 and L2 norm) is tried to measure closeness between two feature vectors, and its impact on retrieval result is analyzed. In this paper, experimental results are presented with several CBIR samples. The current results show that 1) the overall retrieval quantity and quality is improved by combining two types of feature vectors, 2) some feature is better retrieved by a specific feature vector, and 3) retrieval result quality (i.e. ranking of retrieved image tiles) is sensitive to an adopted similarity metric when the extended feature vector is employed.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2000년도 추계종합학술대회논문집
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• pp.153-156
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• 2000

Recently, there has been studied on feature extraction method for efficient content-based image retrieval. Especially, Many researchers have been studying on extracting feature from color Information, because of its advantages. This paper proposes a feature and its extraction method based on color correlogram that is extracted from color information in an image. the proposed method is computed from the image segmented into two parts; the complex part and the plain part. Our experiments show that the performance of the proposed method is better as compared with that of the original color correlogram method.

• 대한산업공학회지
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• 제26권4호
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• pp.363-375
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• 2000

In this paper we propose a content-based image retrieval method that can search large image databases efficiently by color, texture, and shape content. Quantized RGB histograms and the dominant triple (hue, saturation, and value), which are extracted from quantized HSV joint histogram in the local image region, are used for representing global/local color information in the image. Entropy and maximum entry from co-occurrence matrices are used for texture information and edge angle histogram is used for representing shape information. Relevance feedback approach, which has coupled proposed features, is used for obtaining better retrieval accuracy. Simulation results illustrate the above method provides 77.5 percent precision rate without relevance feedback and increased precision rate using relevance feedback for overall queries. We also present a new indexing method that supports fast retrieval in large image databases. Tree structures constructed by k-means algorithm, along with the idea of triangle inequality, eliminate candidate images for similarity calculation between query image and each database image. We find that the proposed method reduces calculation up to average 92.9 percent of the images from direct comparison.

• 제어로봇시스템학회논문지
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• 제5권7호
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• pp.821-826
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• 1999

This paper proposes a face recognition method using feature spaces and image vectors in the image plane. We obtain the 2-D feature space using the self-organizing map which has two inputs from the axis of the given image. The image vector consists of its weights and the average gray levels in the feature space. Also, we can reconstruct an normalized face by using the image vector having no connection with the size of the given face image. In the proposed method, each face is recognized with the best match of the feature spaces and the maximum match of the normally retrieval face images, respectively. For enhancing recognition rates, our method combines the two recognition methods by the feature spaces and the retrieval images. Simulations are conducted on the ORL(Olivetti Research laboratory) images of 40 persons, in which each person has 10 facial images, and the result shows 100% recognition and 14.5% rejection rates for the 20$\times$20 feature sizes and the 24$\times$28 retrieval image size.

• ETRI Journal
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• 제39권1호
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• pp.97-107
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• 2017

A fast and accurate building-level visual place recognition method built on an image-retrieval scheme using street-view images is proposed. Reference images generated from street-view images usually depict multiple buildings and confusing regions, such as roads, sky, and vehicles, which degrades retrieval accuracy and causes matching ambiguity. The proposed practical database refinement method uses informative reference image and keypoint selection. For database refinement, the method uses a spatial layout of the buildings in the reference image, specifically a building-identification mask image, which is obtained from a prebuilt three-dimensional model of the site. A global-positioning-system-aware retrieval structure is incorporated in it. To evaluate the method, we constructed a dataset over an area of $0.26km^2$. It was comprised of 38,700 reference images and corresponding building-identification mask images. The proposed method removed 25% of the database images using informative reference image selection. It achieved 85.6% recall of the top five candidates in 1.25 s of full processing. The method thus achieved high accuracy at a low computational complexity.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권7호
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• pp.323-326
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• 2002

As multimedia database and digital image libraries are enlarged, CBIR(Content Based Image Retrieval) has been getting importance for the efficient search. Generally, CBIR uses primitive features such as color, shape, texture and so on. Among various methods of CBIR, Gabor wavelet has good image retrieval performance with texture features but it has a disadvantage which does not perform well for a rotated image because of its direction oriented filter. In this paper, we propose a new method to solve this problem by modifying Gabor filter for all directions. And then we will compare the searching performance of the proposed method with those of conventional image retrieval methods through experiments with trademarks.

• 전자공학회논문지B
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• 제33B권6호
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• pp.111-118
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• 1996

A content-based image retrieval system is designed and implemetned using the color featurees which are histogram intersection and color pairs. The preprocessor for the image retrieval manage linearly the existing HSI(hue, saturation, saturation, intensity). Hue and intensity histogram thresholding for each color attribute is performed to split the chromatic and achromatic regions respectively. Grouping te indexes produced by the histogram intersection is used to save the retrieval times. Each image is divided into the cells of 32$\times$32 pixels, and color pairs are used to represent the query during retrievals. The recall/precision of histogram intersection is 0.621/0.663 and recall/precision of color pairs is 0.438/0.536. And recall/precision of proposed method is 0.765/0.775/. It is shown that the proposed method using histogram intersection and color pairs improves the retrieval rates.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 제14회 신호처리 합동 학술대회 논문집
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• pp.669-672
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• 2001

We propose an efficient algorithm for an interactive content-based image retrieval using relevance feedback. In the proposed algorithm, a new query feature vector first is yielded from the average feature vector of the relevant images that is fed back from the result images of the previous retrieval. Each component weight of a feature vector is computed from an inverse of standard deviation for each component of the relevant images. The updated feature vector of the query and the component weights are used in the iterative retrieval process. In addition, the irrelevant images are excluded from object images in the next iteration to obtain additional performance improvement. In order to evaluate the retrieval performance of the proposed method, we experiment for three image databases, that is, Corel, Vistex, and Ultra databases. We have chosen wavelet moments, BDIP and BVLC, and MFS as features representing the visual content of an image. The experimental results show that the proposed method yields large precision improvement.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제7권5호
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• pp.510-521
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• 2001

영상은 대용량적인 특성과 비정형적인 특성을 가지고 있으므로 신속하고 효율적으로 영상을 검색하기 위해 영상의 정확한 특징정보를 추출하여 검색 시스템을 구축하여야 한다. 영상 검색 시스템은 텍스트 기반의 전통 데이타베이스와는 다른 모델링 방법과 검색방법을 사용한다. 따라서, 영상 검색 시스템에서의 검색속도와 정확도를 향상시키기 위해서는 새로운 영상 데이타베이스 생성기법과 효율적인 검색 기법이 필요하다. 본 논문에서는 입력 영상으로부터 검색에 상용되는 에지 특징정보 추출을 위해 라플라시 안마스크와 입력 영상을 컨벌루션하여 에지의 외곽선 데이타를 추출하였으며, 그리고 추출한 에지 특징정보와 메타데이타로 영상 데이타베이스를 생성하여 신속하고 효율적으로 영상을 검색할 수 있도록 주석기반 및 내용기반 영상 검색 시스템을 구현하였다. 주석기반 및 내용기반 영상 검색 시스템은 영상의 하위 레벨에 표현된 내용기반 에지 특징정보와 특징정보 추출이 어려운 상위레벨에 표현된 주석기반 에지 특징 정보를 영상의 색인으로 구성하여 사용하기 때문에 영상 컨텐츠 검색의 성능을 향상시킬 수 있다. 마지막으로 본 논문에서 제시한 영상 검색 시스템은 메타데이타에 의해 영상 데이타베이스를 구축하므로 정확한 영상 컨텐츠 정보의 축적관리와 영상의 정보공유 및 재이용이 가능하다.