• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.575-578
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• 2005

Topographical images, in case of aerial or satellite images, are usually similar in colors and textures, and complex in shapes. Thus we have to use shape features of images for efficiently retrieving a query image from topographical image databases. In this paper, we propose a shape feature extraction method which is suitable for topographical images. This method, which improves the existing projection in the Cartesian coordinates, performs the projection operation in the polar coordinates. This method extracts three attributes, namely the number of region pixels, the boundary pixel length of the region from the centroid, the number of alternations between region and background, along each angular direction of the polar coordinates. It extracts the features of complex shape objects which may have holes and disconnected regions. An advantage of our method is that it is invariant to rotation/scale/translation of images. Finally we show the advantages of our method through experiments by comparing it with CSS which is one of the most successful methods in the area of shape feature extraction

• The KIPS Transactions:PartB
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• v.14B no.1 s.111
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• pp.5-12
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• 2007

This paper gives a novel approach that can be represented an image efficiently with its region and shape information together. To do this, we introduced a region-centralized shape descriptor(RCSD) that the size of region only exists at a center point. RCSD consists of circles with three parameters, the distance and the angle between the tenter points, and the diameter, respectively We verified the RCSD parameters to have an information of shape. We can be proved this by reconstructing the shape from the given parameters and evaluated the difference between the its image and the original one. To get this image, we find the estimated points on the contour from the parameters, and connect them by using an interpolation. According to the evaluation, we obtained 88% performance for real images, and showed that it can be used efficiently for representing the binary images. Also we cu make RCSD parameters to be the normalized patterns to have an invariant of its scale or position, and expand them to improve the quality of the performance.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.95-105
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• 2006

This paper proposes a novel approach to represent the image by using shape descriptor having an information of area. The proposed descriptor is a set of vectors, consists of radius, area and direction parameters in the concentrated center point. Due to the area parameter, we know our descriptor can obtain the information of area. Also, we give an extended shape descriptor to get more detailed representation. To do this, we move the center point of our vision to that point for region of interest. By doing so about all of region of interest, we can get our descriptor for detailed information of the image. From more detailed descriptor, it's natural that it's more efficient fur representation, retrievals and so on. We make it the normalized pattern and expand to improve its quality. The proposed method is invariant to scale, position and rotation. The results show that it can be used efficiently for image representation as we can see in retrievals of silhouette images.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.547-552
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• 2014

As social multimedia sites are getting popular such as Flickr and Facebook, the amount of image information has been increasing very fast. So there have been many studies for accurate social image retrieval. Some of them were web image classification using semantic relations of image tags and BoVW(Bag of Visual Words). In this paper, we propose a method to detect salient region in images using GBVS(Graph Based Visual Saliency) model which can eliminate less important region like a background. First, We construct BoVW based on SIFT algorithm from the database of the preliminary retrieved images with semantically related tags. Second, detect salient region in test images using GBVS model. The result of image classification showed higher accuracy than the previous research. Therefore we expect that our method can classify a variety of images more accurately.

• ETRI Journal
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• v.27 no.3
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• pp.294-303
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• 2005

In this paper, a supervised algorithm for the evaluation of geophysical sites using a multi-level cellular neural network (ML-CNN) is introduced, developed, and applied to real data. ML-CNN is a stochastic image processing technique based on template optimization using neighborhood relationships of the pixels. The separation/enhancement and border detection performance of the proposed method is evaluated by various interesting real applications. A genetic algorithm is used in the optimization of CNN templates. The first application is concerned with the separation of potential field data of the Dumluca chromite region, which is one of the rich reserves of Turkey; in this context, the classical approach to the gravity anomaly separation method is one of the main problems in geophysics. The other application is the border detection of archeological ruins of the Hittite Empire in Turkey. The Hittite civilization sites located at the Sivas-Altinyayla region of Turkey are among the most important archeological sites in history, one reason among others being that written documentation was first produced by this civilization.

• Proceedings of the KSRS Conference
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• v.2
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• pp.610-614
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• 2006

In this paper the advanced method of geodesic active contours was developed for the task of building detection from aerial and satellite images. Automatic extraction of man-made structures including buildings, building blocks or roads from remote sensing data is useful for land use mapping, scene understanding, robotic navigation, image retrieval, surveillance, emergency management procedures, cadastral etc. A level set method based on a region-driven segmentation model was implemented with which building boundaries were detected, through this curve propagation technique. The essence of this approach is to optimize the position and the geometric form of the curve by measuring information along that curve, and within the regions that compose the image partition. To this end, one can consider uniform intensities inside objects and the background. Thus, given an initial position of the curve, one can determine global, region-driven functions and provide a statistical description of the inside and outside object area. The calculus of variations and a gradient descent method was used to optimize the variational functional by an iterative steady state process. Experimental results demonstrate the potential of the proposed processing scheme.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.55-62
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• 2021

In this paper, we propose a human-content interface that allows users to quickly and efficiently search data through friction-based scrolling with ROI(Regions of interests). Our approach, conceived from the behavior of finding information or content of interest to users, efficiently calculates ROI for a given content. Based on the kernel developed by conceiving from GMM(Gaussian mixture model), information is searched by moving the screen smoothly and quickly to the location of the information of interest to the user. In this paper, linear interpolation is applied to make one softer inertia, and this is applied to scrolls. As a result, unlike the existing approach in which information is searched according to the user's input, our method can more easily and intuitively find information or content that the user is interested in through friction-based scrolling. For this reason, the user can save search time.

• Journal of KIISE:Databases
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• v.35 no.4
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• pp.345-355
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• 2008

SIFT is popularly used in computer vision application such as object recognition, motion tracking, and 3D reconstruction among various shape descriptors. However, it is not easy to apply SIFT into the image similarity search as it is since it uses many high dimensional keypoint vectors. In this paper, we present a SIFT based image similarity search method using an edge image pyramid and an interesting region detection. The proposed method extracts keypoints, which is invariant to contrast, scale, and rotation of image, by using the edge image pyramid and removes many unnecessary keypoints from the image by using the hough transform. The proposed hough transform can detect objects of ellipse type so that it can be used to find interesting regions. Experimental results demonstrate that the retrieval performance of the proposed method is about 20% better than that of traditional SIFT in average recall.

• Journal of KIISE:Software and Applications
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• v.26 no.12
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• pp.1519-1530
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• 1999

오늘날 멀티미디어 정보의 양이 매우 빠른 속도로 증가함에 따라 멀티미디어 데이타베이스에 대한 효율적인 관리는 더욱 중요한 의미를 가지게 되었다. 게다가 영상과 같은 비 문자형태의 데이타에 대한 사용자들의 내용기반 검색욕구 증가로 인해 비디오 인덱싱에 대한 관심은 더욱 고조되고 있다. 따라서 본 논문에서는 우선적으로 분할된 샷 경계면에서 추출된 대표 프레임과 정지 영상 데이타베이스로부터 유사 영상과 유사 대표 프레임을 검색할 수 있는 환경을 제공한다. 우선적으로 영상에 의한 질의는 기존에 주로 사용되어온 색상 히스토그램방식을 탈피하여 본 논문에서 제안하는 CS와 GS방식을 이용하여 색상 및 방향성 정보도 고려하도록 설계하였다. 또한 얼굴에 의한 질의는 대표 프레임으로부터 얼굴 영역을 추출해 내고 얼굴의 경계선 값 및 쌍 직교 웨이블릿 변환에 의해 얻어진 2개의 특징값을 이용하여 유사 인물이 포함된 대표 프레임을 검색해 내도록 설계하였다. Abstract There is a rapid increase in the use of digital video information in recent years, it becomes more important to manage multimedia databases efficiently. There is a big concern about video indexing because users require content-based image retrieval. In this paper, we first propose query-by-image system environment which allows to retrieve similar images from the chosen representative frames or images from the image databases. This algorithm considers not only the discretized color histogram but also the proposed directional information called CS & GS method. Finally, we designe another query environment using query-by-face. In this system , user selects a people in the representative frame browser and then system extracts a face region from that frame. After that system retrieves similar representative frames using 2 features, edge information and biorthogonal wavelet transform.

• Journal of Korea Multimedia Society
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• v.7 no.5
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• pp.648-657
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• 2004

A method of extracting central objects in color images without any prior-knowledge is proposed in this paper, which uses basically information of significant color distribution. A central object in an image is defined as a set of regions that lie around center of the image and have significant color distribution against the other surround (or background) regions. Significant colors in an image are first defined as the colors that are distributed more densely around center of the image than near borders. Then core object regions (CORs) are selected as the regions a lot of pixels of which have the significant colors. Finally, the adjacent regions to the CORs are iteratively merged if they are similar to the CORs but not to the background regions in color distribution. The merging result is accepted as the central object that may include differently color-characterized regions and/or two or more objects of interest. Usefulness of the significant colors in extracting the central object was verified through experiments on several kinds of test images. We expect that central objects shall be used usefully in image retrieval applications.