• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 1996년도 학술대회
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• pp.63-66
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• 1996

In this paper, we propose a new content-based color image retrieval algorithm. The algorithm makes use of two features; colors as global features and block classification results as local features. More specifically, we obtain R, G, B color histograms and classify nonoverlapping small image blocks into texture, monotone, and various edges, then using these histograms and classification results were make a similarity measure. Experimental results show that retrieval rate of the proposed algorithm is higher than the previous method.

• 전기학회논문지
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• 제65권12호
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• pp.2270-2277
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• 2016

In modern society, creation and distribution of multimedia contents is being actively conducted. These multimedia information have come out the enormous amount daily, the amount of data is also large enough it can't be compared with past text information. Since it has been increased for a need of the method to efficiently store multimedia information and to easily search the information, various methods associated therewith have been actively studied. In particular, image search methods for finding what you want from the video database or multiple sequential images, have attracted attention as a new field of image processing. Image retrieval method to be implemented in this paper, utilizes the attribute of corner patches based on the corner points of the object, for providing a new method of efficient and robust image search. After detecting the edge of the object within the image, the straight lines using a Hough transformation is extracted. A corner patches is formed by defining the extracted intersection of the straight line as a corner point. After configuring the feature vectors with patches rearranged, the similarity between images in the database is measured. Finally, for an accurate comparison between the proposed algorithm and existing algorithms, the recall precision rate, which has been widely used in content-based image retrieval was used to measure the performance evaluation. For the image used in the experiment, it was confirmed that the image is detected more accurately in the proposed method than the conventional image retrieval methods.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 제14회 신호처리 합동 학술대회 논문집
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• pp.187-190
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• 2001

The application of one-dimensional projection to each image enables to obtain shape or spatial information of image. This paper proposes a method that uses relative distances between peaks and their maximum value in the projection vector. In order to verify retrieval performance, the experimental results between the histogram intersection method, the projection only method. and the proposed one are compared and analyzed.

• 한국컴퓨터정보학회논문지
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• 제11권4호
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• pp.1-7
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• 2006

최근 이미지 검색은 검색의 정확성을 높이고자 사용자의 요구를 반영하는 적합성 피드백에 관한 연구가 활발히 진행되고 있다. 본 논문은 이미지 검색 시 나타나는 고수준 개념과 저수준 특징 사이의 의미적 격차를 줄이기 위하여 적합성 피드백에 기반한 영역 기반 이미지 검색의 가중치 기법에 대해서 논의하고 새로운 가중치 기법을 제안한다. 새롭게 제시된 가중치 기법은 한 이미지에 존재하는 영역들의 공간적 위치에 따라 영역의 중요성을 결정한다. 실험 결과는 본 논문에서 제시된 가중치 기법이 평균 재현율에 있어서 크기 백분율 가중치 기법에 비해 약 18%, 역 이미지 빈도수를 적용한 영역 빈도수 가중치 기법에 비해 약 11% 가량 높게 나타나는 것을 보이고 있으며, 검색 시간에 있어서도 영역 빈도수 가중치에 비해 약 1/10인 것을 보이고 있다.

• ETRI Journal
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• 제24권1호
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• pp.23-30
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• 2002

MPEG-7 Visual Standard specifies a set of descriptors that can be used to measure similarity in images or video. Among them, the Edge Histogram Descriptor describes edge distribution with a histogram based on local edge distribution in an image. Since the Edge Histogram Descriptor recommended for the MPEG-7 standard represents only local edge distribution in the image, the matching performance for image retrieval may not be satisfactory. This paper proposes the use of global and semi-local edge histograms generated directly from the local histogram bins to increase the matching performance. Then, the global, semi-global, and local histograms of images are combined to measure the image similarity and are compared with the MPEG-7 descriptor of the local-only histogram. Since we exploit the absolute location of the edge in the image as well as its global composition, the proposed matching method can retrieve semantically similar images. Experiments on MPEG-7 test images show that the proposed method yields better retrieval performance by an amount of 0.04 in ANMRR, which shows a significant difference in visual inspection.

• 반도체디스플레이기술학회지
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• 제10권3호
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• pp.75-81
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• 2011

This paper presents a novel method for image classification based on a hybrid genetic algorithm (GA) and support vector machine (SVM) approach which can significantly improve the classification performance for content-based image retrieval (CBIR). Though SVM has been widely applied to CBIR, it has some problems such as the kernel parameters setting and feature subset selection of SVM which impact the classification accuracy in the learning process. This study aims at simultaneously optimizing the parameters of SVM and feature subset without degrading the classification accuracy of SVM using GA for CBIR. Using the hybrid GA and SVM model, we can classify more images in the database effectively. Experiments were carried out on a large-size database of images and experiment results show that the classification accuracy of conventional SVM may be improved significantly by using the proposed model. We also found that the proposed model outperformed all the other models such as neural network and typical SVM models.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.745-746
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• 2008

In the last few years rapid improvements in hardware technology have made it possible to process, store and retrieve huge amounts of data ina multimedia format. As a result, Content-Based Image Retrieval(CBIR) has been receiving widespred interest during the last decade. This paper propose the content-based retrieval system as a method for performing image retrieval throught the effective feature analysis of the object of significant meaning by using texture descriptor.

• ETRI Journal
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• 제29권5호
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• pp.700-702
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• 2007

We present a relevance feedback approach based on multi-class support vector machine (SVM) learning and cluster-merging which can significantly improve the retrieval performance in region-based image retrieval. Semantically relevant images may exhibit various visual characteristics and may be scattered in several classes in the feature space due to the semantic gap between low-level features and high-level semantics in the user's mind. To find the semantic classes through relevance feedback, the proposed method reduces the burden of completely re-clustering the classes at iterations and classifies multiple classes. Experimental results show that the proposed method is more effective and efficient than the two-class SVM and multi-class relevance feedback methods.

• 반도체디스플레이기술학회지
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• 제7권4호
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• pp.13-18
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• 2008

This paper explores a content-based image retrieval framework with relevance feedback based on genetic algorithm (GA). This framework adopts GA to learn the user preferences using the similarity functions defined for all available descriptors. The objective of the GA-based learning methods is to learn the user preferences using the similarity functions and to find a descriptor combination function that best represents the user perception. Experiments were performed to validate the proposed frameworks. The experiments employed the natural image databases and color and texture descriptors to represent the content of database images. The proposed frameworks were compared with the other two relevance feedback methods regarding effectiveness in image retrieval tasks. Experiment results demonstrate the superiority of the proposed method.

• 한국정보과학회논문지:소프트웨어및응용
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• 제32권11호
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• pp.1013-1020
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• 2005

본 논문은 모양 특성을 이용한 효과적인 식물 잎 이미지 검색 시스템을 제시한다. 잎 이미지의 더 효과적인 표현을 위해 개선된 MPP 알고리즘을 제안하고, 매칭에 소요되는 시간을 줄이기 위해 기존의 Nearest Neighbor(NN) 검색을 수정한 동적인 매칭 알고리즘을 제시한다. 특히, 더 나은 정확율과 효율성을 위해, 잎 모양과 잎차례를 스케치하여 질의할 수 있도록 하였다. 실험에서는 제안한 알고리즘과 기존의 알고리즘인 CCD(Centroid Contour Distance), Fourier Descriptor. Curvature Scale Space Descriptor (CSSD), Moment Invariants, MPP와 비교하였다. 1000여개의 식물 잎 이미지를 통한 실험결과는 제안한 방법이 기존의 기법보다 더 좋은 성능임을 보였다.