• 한국컴퓨터정보학회논문지
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• 제9권2호
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• pp.19-26
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• 2004

본 논문에서는 내용 기반 영상 검색 시스템(Content Based Image Retrieval System)의 특징 추출(feature extraction)과 분석(analysis)을 위한 방법으로 적응적 컴포넌트 분석(ACA: Adaptive Component Analysis)을 제안하고 있다. 검색을 위해서 영상에서 추출된 특징들은 영상의 도메인(domain)에 따라 적절하게 적용해야만 좋은 검색 결과를 얻을 수 있다. 이러한 조건을 만족시키기 위한 방법으로 본 논문에서는 검색 측정도(retrieval measurement)를 제안하고 있다. ACA는 알고리즘과 시스템적인 관점에서 볼 때, 기존의 내용 기반 영상 검색을 위한 중간 단계라고 할 수 있으며, 검색 속도향상 및 성능 개선에 목표를 두고 있다

• 한국컴퓨터정보학회지
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• 제12권1호
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• pp.9-19
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• 2004

본 논문에서는 내용 기반 영상 검색 시스템(Content Based Image Retrieval System)의 특징 추출(feature extraction)과 분석(analysis)을 위한 방법으로 적응적 컴포넌트 분석(ACA: Adaptive Component Analysis)을 제안하고 있다. 검색을 위해서 영상에서 추출된 특징들은 영상의 도메인(domain)에 따라 적절하게 적용해야만 좋은 검색 결과를 얻을 수 있다. 이러한 조건을 만족시키기 위한 방법으로 본 논문에서는 검색 측정도(retrieval measurement)를 제안하고 있다. ACA는 알고리즘과 시스템적인 관점에서 볼 때, 기존의 내용 기반 영상 검색을 위한 중간 단계라고 할 수 있으며, 검색 속도 향상 및 성능 개선에 목표를 두고 있다.

• 제어로봇시스템학회논문지
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• 제14권5호
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• pp.473-482
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• 2008

In this paper we propose an expert system for content-based image retrieval with object database. The proposed system finds keyword by using knowledge-base and feature of extracted object, and retrieves image by using keyword based image retrieval method. The system can decrease error of image retrieval and save running time. The system also checks whether similar objects exist or not. If not, user can store information of object in object database. Proposed system is flexible and extensible, enabling experts to incrementally add more knowledge and information. Experimental results show that the proposed system is more effective than existing content-based image retrieval method in running time and precision.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.451-454
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• 2003

A new query method, called query by visual keys, is proposed to aim easy operation and efficient region-based image retrieval (RBIR). Visual keys are constructed from representative regions/subimages in a given image database, and the database is indexed with visual keys. A system on PC is presented, where text retrieval techniques are applied to the image retrieval with visual keys. Experimental results show that one retrieval is done within 4ms and that the proposed system achieves the comparable retrieval precision (with user-friendly operation and low computational cost) to conventional region based image retrieval systems

• 한국정보처리학회논문지
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• 제7권2S호
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• pp.678-691
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• 2000

A variety of multimedia applications require multimedia database management systems to manage multimedia data, such as text, image, and video, as well as t support content-based image or video retrieval. In this paper we design and implement a content-based image retrieval system, BADA-IV/I$^2$R(Image Information Retrieval), which is developed based on BADA-IV multimedia database management system. In this system image databases can be efficiently constructed and retrieved with the visual features, such as color, shape, and texture, of image. we extend SQL statements to define image query based on both annotations and visual features of image together. A high-dimensional index structure, called CIR-tree, is also employed in the system to provide an efficient access method to image databases. We show that BADA-IV/I$^2$R provides a flexible way to define query for image retrieval and retrieves image data fast and effectively: the effectiveness and performance of image retrieval are shown by BEP(Bull's Eye Performance) that is used to measure the retrieval effectiveness in MPEG-7 and comparing the performance of CIR-tree with those of X-tree and TV-tree, respectively.

• 제어로봇시스템학회논문지
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• 제10권7호
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• pp.597-603
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• 2004

In retrieving large database of image data, the clustering is essential for fast retrieval. However, it is difficult to cluster a number of image data adequately. Moreover, current retrieval methods using similarities are uncertain of retrieval accuracy and take much retrieving time. In this paper, a suggested image retrieval system combines Fuzzy ART neural network algorithm to reinforce defects and to support them efficiently. This image retrieval system takes color and texture as specific feature required in retrieval system and normalizes each of them. We adapt Fuzzy ART algorithm as neural network which receive normalized input-vector and propose improved Fuzzy ART algorithm. The result of implementation with 200 image data shows approximately retrieval ratio of 83%.

• 산업경영시스템학회지
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• 제27권3호
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• pp.59-65
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• 2004

This paper explores an image segmentation and representation method using Vector Quantization(VQ) on color and texture for content-based image retrieval system. The basic idea is a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture space. These schemes are used for object-based image retrieval. Features for image retrieval are three color features from HSV color model and five texture features from Gray-level co-occurrence matrices. Once the feature extraction scheme is performed in the image, 8-dimensional feature vectors represent each pixel in the image. VQ algorithm is used to cluster each pixel data into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to object within the image. The proposed method can retrieve similar images even in the case that the objects are translated, scaled, and rotated.

• 한국멀티미디어학회논문지
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• 제6권4호
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• pp.746-752
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• 2003

In this paper, we proposed an image retrieval system with a simple and effective relevance feedback, called RAP(Reward and Punishment) algorithm. First, color and texture features were extracted from the images. Next, the extracted feature values were used for image retrieval in various forms. We applied the relevance feedback to the initial retrieved images from the image retrieval system, and compared its result with that of the conventional system. In the experiment using the test image database of 16 class 512 images, the proposed system showed the better retrieval performance of about 10∼l7 % than that of the conventional INRIA system in each relevance feedback step.

• 한국컴퓨터정보학회지
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• 제15권1호
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• pp.185-190
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• 2007

An image retrieval system is a piece of software that searches identical or similar images based on various image-specific features. This paper proposes a trademark image retrieval system that uses image colors and forms. In the proposed system, input images are segmented into several other regions, and color distribution histograms for different regions are extracted for use as color information. The proposed system uses form information through the preprocessing process such as boundary surface extraction, centroid extraction, angular sampling and, and through calculating the sums of the distances between the centroid and the boundary surfaces, standard deviations, and the ratios between long and short axes. Like this, the color and form information extracted is used to perform retrieval through measuring similarity.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.683-688
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• 2008

The emotional information processing is to simulate and recognize human sensibility, sensuality or emotion, to realize natural and harmonious human-machine interface. This paper proposes an emotion-based image retrieval method. In this method, user can choose a linguistic query among some emotional adjectives. Then the system shows some corresponding representative images that are pre-evaluated by experts. Again the user can select a representative one among the representative images to initiate traditional content-based image retrieval (CBIR). By this proposed method any CBIR can be easily expanded as emotion-based image retrieval. In CBIR of our system, we use several color and texture visual descriptors recommended by MPEG-7. We also propose a fuzzy similarity measure based on Choquet integral in the CBIR system. For the communication between system and user, a relevance feedback mechanism is used to represent human subjectivity in image retrieval. This can improve the performance of image retrieval, and also satisfy the user's individual preference.