• Journal of KIISE:Databases
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• v.36 no.2
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• pp.150-167
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• 2009

XML has become the new standard for storing, exchanging, and publishing of data over both the internet and the ubiquitous data stream environment. As demand for efficiency in handling XML document grows, labeling scheme has become an important topic in data storage. Recently proposed labeling schemes reflect the dynamic XML environment, which itself provides motivation for the discovery of an efficient labeling scheme. However, previous proposed labeling schemes have several problems: 1) An insertion of a new node into the XML document triggers re-labeling of pre-existing nodes. 2) They need larger memory space to store total label. etc. In this paper, we introduce a new labeling scheme called a Circle Labeling Scheme. In CLS, XML documents are represented in a circular form, and efficient storage of labels is supported by the use of concepts Rotation Number and Parent Circle/Child Circle. The concept of Radius is applied to support inclusion of new nodes at arbitrary positions in the tree. This eliminates the need for re-labeling existing nodes and the need to increase label length, and mitigates conflict with existing labels. A detailed experimental study demonstrates efficiency of CLS.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.129-137
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• 2006

An XML labeling scheme is an efficient encoding method to determine the ancestor-descendant relationships of elements and the orders of siblings. Recently, many dynamic XML documents have appeared in the Web Services and the AXML(the Active XML), so we need to manage them with a dynamic XML labeling scheme. The prime number labeling scheme is a representative scheme which supports dynamic XML documents. It determines the ancestor-descendant relationships between two elements with the feature of prime numbers. When a new element is inserted into the XML document using this scheme, it has an advantage that an assigning the label of new element don't change the label values of existing nodes. But it has to have additional expensive operations and data structure for maintaining the orders of siblings. In this paper, we suggest the order number sharing method and algorithms categorized by the insertion positions of new nodes. They greatly minimize the existing method's sibling order maintenance cost.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2219-2225
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• 2008

Labeling schemes which don't consider about frequent update in dynamic XML documents need relabeling process to reflect the changed label information whenever the tree of XML document is update. There is disadvantage of considerable expenses in the dynamic XML document which can occurs frequent update. To solve this problem, we suggest prime number labeling scheme that doesn't need relabeling process. However the prime number labeling scheme does not consider that it needs to update the sibling order of nodes in the XML tree of document. This update process needs much costs because the most of the XML tree of document has to be relabeling and recalculation. In this paper, we propose the prime number labeling scheme with sibling order value that can maintain the sibling order without relabeling or recalculation the XML tree of documents.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.55-57
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• 2014

본 논문에서는 자동차 번호판 인식을 위해 직선검출법, 모폴로지에 의한 검출법을 사용하지 않고, Blob 레이블링 기법을 이용한 번호판 인식 기법을 제안한다. 고성능 컴퓨팅 시스템의 성능 향상을 위한 효율적인 동적 작업부하 균등화 정책을 제안한다. ITS분야에서 가장 중요한 요소라 할 수 있는 자동차 번호판 인식은 자동화된 차량 관리 시스템 구성에 필수적인 요소로 요구된다. 또한, 자동차와 관련된 정보는 직, 간접적으로 높은 중요도를 가지고 있으며, 자동차와 관련된 정보가 이용되는 영역은 교통관리, 교통량분석, 자동 요금 징수 시스템, 자동차 위법 단속 등 응용범위가 나날이 넓어지고 있다. 본 논문에서는 자동차 번호판 인식을 위해 Blob 레이블링 기법을 이용하였으며, 번호판 인식을 위한 영상 샘플은 오츠알고리즘을 이용하여 이진화된 영상을 사용하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.8
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• pp.1495-1504
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• 2008

New license plates have been used since 2007. This paper proposes a new license plate extraction method using a gray labeling and a fuzzy reasoning method. First, the proposed method extracts the candidate plates by the gray labeling which is the enhanced version of a non-recursive flood-filling algorithm. By newly designed fuzzy inference system. fitness of each candidate plates are calculated. Finally, the area of the license plate in a image is extracted as a region of the candidate label which has the highest fitness. In the experiments, various license plate images took from indoor/outdoor parking lot, street, etc. by digital camera or cellular phone were used and the proposed extraction method was showed remarkable results of a 94 percent success.

• Journal of KIISE
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• v.41 no.9
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• pp.699-706
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• 2014

As XML documents are getting bigger and more complex, a keyword-based search method that does not require structural information is needed to search these large XML documents. In order to use this method, not only all keywords expressed as nodes in the XML document must be labeled for indexing but also structural information should be well represented. However, the existing labeling methods either have very simple information of XML documents for index or represent the structural information which is difficult to deal with the increase of XML documents' size. As the size of XML documents is getting larger, it causes either the poor performance of keyword search or the exponential increase of space usage. In this paper, we present the Repetitive Prime Labeling Scheme (RPLS) in order to improve the problem of the existing labeling methods for keyword-based search of large XML documents. This method is based on the existing prime number labeling method and allows a parent's prime number to be used at a lower level repeatedly so that the number of prime numbers being generated can be reduced. Then, we show an experimental result of the comparison between our methods and the existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1355-1360
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• 2020

In a map, placing the labels, corresponding to names or explanations of specific features, is called map labeling. In this paper, n points on a line are given, and placing rectangular labels for the points is considered. Particularly, the labels have a same height and their lower sides lie on a straight line in the upper of the line on which the given points are. The points and the labels are connected by polygonal lines, which are called leader lines. The leader lines are classified into straight leader lines and bended leader lines, where the straight leader line consists of only the vertical line and the bended leader line consists of vertical, horizontal, vertical lines. The problem is placing the labels to minimize the number of bended leader lines, and we propose an O(nlogn)-time algorithm, which improves the O(n2)-time algorithm previously provided in [13].

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.701-715
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• 2021

The drastic advance of recent deep learning technologies is heavily dependent on training datasets which are essential to train models by themselves with less human efforts. In comparison with the work to design deep learning models, preparing datasets is a long haul; at the moment, in the domain of vision intelligent, datasets are still being made by handwork requiring a lot of time and efforts, where workers need to directly make labels on each image usually with GUI-based labeling tools. In this paper, we overview the current status of vision datasets focusing on what datasets are being shared and how they are prepared with various labeling tools. Particularly, in order to relieve the repetitive and tiring labeling work, we present an interactive smart image annotating system with which the annotation work can be transformed from the direct human-only manual labeling to a correction-after-checking by means of a support of automatic labeling. In an experiment, we show that automatic labeling can greatly improve the productivity of datasets especially reducing time and efforts to specify regions of objects found in images. Finally, we discuss critical issues that we faced in the experiment to our annotation system and describe future work to raise the productivity of image datasets creation for accelerating AI technology.

• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.386-398
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• 2020

We developed an integrative annotation program that can perform data labeling process for deep learning models in object recognition. The program utilizes the basic GUI library of Python and configures crawler functions that allow data collection in real time. Retinanet was used to implement an automatic annotation function. In addition, different data labeling formats for Pascal-VOC, YOLO and Retinanet were generated. Through the experiment of the proposed method, a domestic vehicle image dataset was built, and it is applied to Retinanet and YOLO as the training and test set. The proposed system classified the vehicle model with the accuracy of about 94%.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.3
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• pp.16-24
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• 2019

Recently, research on emotion recognition based on EEG has attracted great interest from human-robot interaction field. In this paper, we propose a method of labeling using image-based EEG topography instead of evaluating emotions through self-assessment and annotation labeling methods used in MAHNOB HCI. The proposed method evaluates the emotion by machine learning model that learned EEG signal transformed into topographical image. In the experiments using MAHNOB-HCI database, we compared the performance of training EEG topography labeling models of SVM and kNN. The accuracy of the proposed method was 54.2% in SVM and 57.7% in kNN.