• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.2
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• pp.54-68
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• 2002

In this paper, We propose a multi-dimensional index structure, called a VA (vector approximate) -tree that constructs a tree with vector approximates of multi-dimensional feature vectors. To save storage space for index structures, the VA-tree employs vector approximation concepts of VA-file that presents feature vectors with much smaller number of bits than original value. Since the VA-tree is a tree structure, it does not suffer from performance degradation owing to the increase of data. Also, even though the VA-tree is MBR Minimum Bounding Region) based tree structure like a R-tree, its split algorithm never allows overlap between MBRs. We show through various experiments that our proposed VA-tree is the efficient index structure for large amount of multi-dimensional data.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.29 no.3
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• pp.297-320
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• 2018

This study aims to improve the access and retrieval of images and to find a way to effectively generate tags as a tool for providing explanation of images. To do this, this study investigated the features of human tagging and machine tagging, and compare and analyze them. Machine tags had the highest general attributes, some specific attributes and visual elements, and few abstract attributes. The general attribute of the human tag was the highest, but the specific attribute was high for the object and scene where the human tag constructor can recognize the name. In addition, sentiments and emotions, as well as subjects of abstract concepts, events, places, time, and relationships are represented by various tags. The tag set generated through this study can be used as basic data for constructing training data set to improve the machine learning algorithm.

• Journal of the Korean Society for Library and Information Science
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• v.48 no.1
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• pp.53-73
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• 2014

Given that emotion-based computing environment has grown recently, it is necessary to focus on emotional access and use of multimedia resources including images. The purpose of this study aims to identify the visual cues for emotion in images. In order to achieve it, this study selected five basic emotions such as love, happiness, sadness, fear, and anger and interviewed twenty participants to demonstrate the visual cues for emotions. A total of 620 visual cues mentioned by participants were collected from the interview results and coded according to five categories and 18 sub-categories for visual cues. Findings of this study showed that facial expressions, actions / behaviors, and syntactic features were found to be significant in terms of perceiving a specific emotion of the image. An individual emotion from visual cues demonstrated distinctive characteristics. The emotion of love showed a higher relation with visual cues such as actions and behaviors, and the happy emotion is substantially related to facial expressions. In addition, the sad emotion was found to be perceived primarily through actions and behaviors and the fear emotion is perceived considerably through facial expressions. The anger emotion is highly related to syntactic features such as lines, shapes, and sizes. Findings of this study implicated that emotional indexing could be effective when content-based features were considered in combination with concept-based features.

• Journal of the Korea Computer Graphics Society
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• v.8 no.2
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• pp.15-22
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• 2002

Image segmentation methods partition an image into meaningful regions. For image composition and analysis, it is desirable for the partitioned regions to represent meaningful objects in terms of human perception and manipulation. Despite the recent progress in image understanding, however, most of the segmentation methods mainly employ low-level image features and it is still highly challenging to automatically segment an image based on high-level meaning suitable for human interpretation. The concept of HCI (Human Computer Interaction) can be applied to operator-assisted image segmentation in a manner that a human operator provides guidance to automatic image processing by interactively supplying critical information about object boundaries. Intelligent Scissors and Snakes have demonstrated the effectiveness of human-assisted segmentation [2] [1]. This paper presents a method for interactive image segmentation for more efficient and effective detection and tracking of object boundaries. The presented method is partly based on the concept of Intelligent Scissors, but employs the well-established Canny edge detector for stable edge detection. It also uses "sewing method" for including weak edges in object boundaries, and 5-direction search to promote more efficient and stable linking of neighboring edges than the previous methods.

• Journal of Information Technology and Architecture
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• v.10 no.1
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• pp.79-91
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• 2013

This paper proposes a systematic methodology that could be used to decide which one shows the best performance among space filling curves (SFCs) in applying lower-dimensional transformations to histogram sequences. A histogram sequence represents a time-series converted from an image by the given SFC. Due to the high-dimensionality nature, histogram sequences are very difficult to be stored and searched in their original form. To solve this problem, we generally use lower-dimensional transformations, which produce lower bounds among high dimensional sequences, but the tightness of those lower-bounds is highly affected by the types of SFC. In this paper, we attack a challenging problem of evaluating which SFC shows the better performance when we apply the lower-dimensional transformation to histogram sequences. For this, we first present a concept of spatial locality, which comes from an intuition of "if the entries are adjacent in a histogram sequence, their corresponding cells should also be adjacent in its original image." We also propose spatial locality preservation metric (slpm in short) that quantitatively evaluates spatial locality and present its formal computation method. We then evaluate five SFCs from the perspective of slpm and verify that this evaluation result concurs with the performance evaluation of lower-dimensional transformations in real image matching. Finally, we perform k-NN (k-nearest neighbors) search based on lower-dimensional transformations and validate accuracy of the proposed slpm by providing that the Hilbert-order with the highest slpm also shows the best performance in k-NN search.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.20 no.5
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• pp.10-16
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• 2002

디지털 기술의 급속한 발전에 의한 디지털 방송의 도입은 방송환경에 커다란 변혁을 초래하고 있다. 기술의 발전은 방송, 통신, 컴퓨터를 융합시킨 새로운 미디어 서비스를 등장시키고 있으며, 이러한 미디어의 융합은 지금까지 독자적인 영역에서 발전하여 오던 각각의 미디어들이 가지고 있는 한계성 또는 단점을 상호보완적으로 개선함으로써 서비스 능력을 대폭적으로 향상시키는 방향으로 전개되고 있다. 이와같은 디지털 시대의 대변혁은 멀티미디어 서비스를 그 중심축으로 하고 있으며, 이른바 방송통신 융합시대의 경쟁력 있는 서비스를 도입하기 위한 미디어간, 국가간의 경계영역을 초월한 글로벌 경쟁체제가 급속히 진전되어 가고 있다. 이와 같은 미디어의 융합 현상에 따라 제작 환경도 새로운 디지털 방송 서비스를 효율적으로 지원하도록 전환되어야 한다. 디지털 방송의 특징인 고품질 및 다채널 시대를 맞아 방송 콘텐츠의 재활용도를 높이는 것은 매우 중요하며, 데이터 방송, 인터넷 방송 등 다양한 기능의 서비스를 도입하기 위해선 제작 체계의 개선이 뒷받침되어야 한다. 따라서 본 논문에서는 새로운 제작 환경에 대해서 살펴보고자 한다. 특히, 컴퓨터 기반의 제작 환경 구축에 핵심 요소인 넌리니어 편집시스템과 방송 콘텐츠를 효율적으로 관리하고 재활용할 수 있는 디지털 아카이브에 대해 소개하고자 한다. 테이프 대신 하드디스크를 기록 매체로 하는 넌리니어(non-linear) 시스템의 등장에 따라 방송 프로그램 제작에 대한 새로운 개념의 정립이 필요하게 되었으며, 이를 근간으로 하는 새로운 프로그램 제작환경, 즉 네트워크에 의해 컴퓨터와 데이터베이스를 연결하여 제작으로부터 송출가지 통합관리가 가능한 새로운 방송 프로그램 제작환경, 이른바 멀티미디어 제작환경을 구축하는 것이 디지털 방송 시대의 방송 사업자에게 가장 중요한 과제중의 하나가 되었다. 멀티미디어 제작환경을 구축함으로써 영상, 음성 및 다양한 부가 데이터를 포함하는 멀티미디어 프로그램을 편리하게 제작할 수 있으며, 데이터베이스로부터 필요한 영상 이미지를 자유롭게 합성, 조작하는 등, 매우 다양하고 편리한 제작기법을 활용할 수 있다. 또한 멀티미디어를 응용한 제작 분야로서 컴퓨터 그래픽스 기술은 방송의 사전제작에 커다란 기여를 하고 있으며, 이미 선거방송을 비롯한 여러가지 프로그램은 가상스튜디오와 가상캐릭터 기술을 활용하여 제작하고 있다. 방송사업자는 이러한 멀티미디어 제작시스템을 근간으로 영상검색, 영상 합성, 스크립트 편집, 가상현실 응용 등 고도의 제작 기법을 활용함으로써 사용자 친화성, 다이나믹한 표현, 실시간, 대화성을 특징으로 하는 다양한 멀티미디어 서비스를 시청자에게 제공할 수 있을 것이다.

• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1043-1054
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• 2009

As the semantic web receives higher concern with an intensified necessity in these days, the research on the ontology as its core technology has been carried out in various fields. The ontology has been adopted as an alternative to work out lots of problematic issues resulted from the insufficient vocabulary selection rules in folksonomy, widely accepted under Web 2.0. Therefore the importance of research to complementarily consolidate the two disciplines, the folksonomy and the ontology, has been increased. Based on this idea this research proposes a system, which pulls out, using open services, the location information tags from folksonomy-based metadata, ultimately extracts, following location information analyses, spatial relationships among tags, and in turn automatically constructs self-correcting location information domain ontology. The system devised in this study will associate data derived from easily accessible folksonomy with meaningful and technological information from ontology.