• 제목/요약/키워드: Content Classification

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A Study on the Revision Process Improvement Plan through the Analysis of the Current Status of the Academic Standard Classification System and Issues

  • Younghee Noh;Jeong-Mo Yang;Ji Hei Kang;Yong Hwan Kim;Jongwook Lee;Woojung Kwak
    • International Journal of Knowledge Content Development & Technology
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    • 제13권1호
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    • pp.111-130
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    • 2023
  • There are the national science and technology standard classification system used in Korea, the classification according to the standard classification system for educational organization units, and the Korean standard education classification by the National Statistical Office. It is not suitable for calculation or evaluation, and classification is still mixed depending on the purpose of use. Therefore, in this study, the current status of academic standard classification, issues related to the standard classification system such as research foundation associations and research institutes, and issues related to the academic standard classification through the analysis of existing prior research issues, etc. As a result of the research, first, it is necessary to maintain and strengthen the linkage of the academic classification system, such as maintaining the linkage between the relevant departmental classification systems and strengthening the linkage with the relevant classification system, as a result of analysis of major issues in the academic standard classification system, and the systematic improvement cycle of the revision process and management system and settings are required.

머신러닝 기반의 디지털 방송 프로그램 유형 분류 및 활용 방안 연구 (A Study of the Classification and Application of Digital Broadcast Program Type based on Machine Learning)

  • 윤상혁;이소현;김희웅
    • 지식경영연구
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    • 제20권3호
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    • pp.119-137
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    • 2019
  • With the recent spread of digital content, more people have been watching the digital content of TV programs on their PCs or mobile devices, rather than on TVs. With the change in such media use pattern, genres(types) of broadcast programs change in the flow of the times and viewers' trends. The programs that were broadcast on TVs have been released in digital content, and thereby people watching such content change their perception. For this reason, it is necessary to newly and differently classify genres(types) of broadcast programs on the basis of digital content, from the conventional classification of program genres(types) in broadcasting companies or relevant industries. Therefore, this study suggests a plan for newly classifying broadcast programs through using machine learning with the log data of people watching the programs in online media and for applying the new classification. This study is academically meaningful in the point that it analyzes and classifies program types on the basis of digital content. In addition, it is meaningful in the point that it makes use of the program classification algorithm developed in relevant industries, and especially suggests the strategy and plan for applying it.

Mapping of the Universe of Knowledge in Different Classification Schemes

  • Satija, M.P.;Martinez-Avila, Daniel
    • International Journal of Knowledge Content Development & Technology
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    • 제7권2호
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    • pp.85-105
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    • 2017
  • Given the variety of approaches to mapping the universe of knowledge that have been presented and discussed in the literature, the purpose of this paper is to systematize their main principles and their applications in the major general modern library classification schemes. We conducted an analysis of the literature on classification and the main classification systems, namely Dewey/Universal Decimal Classification, Cutter's Expansive Classification, Subject Classification of J.D. Brown, Colon Classification, Library of Congress Classification, Bibliographic Classification, Rider's International Classification, Bibliothecal Bibliographic Klassification (BBK), and Broad System of Ordering (BSO). We conclude that the arrangement of the main classes can be done following four principles that are not mutually exclusive: ideological principle, social purpose principle, scientific order, and division by discipline. The paper provides examples and analysis of each system. We also conclude that as knowledge is ever-changing, classifications also change and present a different structure of knowledge depending upon the society and time of their design.

A Novel Image Classification Method for Content-based Image Retrieval via a Hybrid Genetic Algorithm and Support Vector Machine Approach

  • Seo, Kwang-Kyu
    • 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.

Music Genre Classification Based on Timbral Texture and Rhythmic Content Features

  • Baniya, Babu Kaji;Ghimire, Deepak;Lee, Joonwhon
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2013년도 춘계학술발표대회
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    • pp.204-207
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    • 2013
  • Music genre classification is an essential component for music information retrieval system. There are two important components to be considered for better genre classification, which are audio feature extraction and classifier. This paper incorporates two different kinds of features for genre classification, timbral texture and rhythmic content features. Timbral texture contains several spectral and Mel-frequency Cepstral Coefficient (MFCC) features. Before choosing a timbral feature we explore which feature contributes less significant role on genre discrimination. This facilitates the reduction of feature dimension. For the timbral features up to the 4-th order central moments and the covariance components of mutual features are considered to improve the overall classification result. For the rhythmic content the features extracted from beat histogram are selected. In the paper Extreme Learning Machine (ELM) with bagging is used as classifier for classifying the genres. Based on the proposed feature sets and classifier, experiment is performed with well-known datasets: GTZAN databases with ten different music genres, respectively. The proposed method acquires the better classification accuracy than the existing approaches.

패싯분석 기법을 적용한 방송자료의 내용 구조화에 관한 연구: 시사보도 뉴스 프로그램을 대상으로 (A Faceted Classification Analysis of TV content: Using News and Current Affairs Programs)

  • 심지영
    • 정보관리학회지
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    • 제31권3호
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    • pp.313-329
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    • 2014
  • 본 연구에서는 방송자료에 대한 지적 접근점을 제공하기 위한 방편으로, 뉴스 및 시사보도 프로그램의 내용 기술을 위한 패싯 분석 기법의 적용을 시도하였다. 랑가나단의 PMEST 기본 패싯에 기반하여, 보도 장르에 적합한 기본 패싯-'누가', '무엇을', '어떻게', '어디서', '언제'-을 생성하였으며, 보도 장르의 형식적 구조와 내용적 요소를 반영하여 패싯의 구성요소를 추출하였다. 이를 실제 방영한 시사보도 프로그램을 대상으로 적용해 본 결과, 본 연구에서 제안한 패싯이 보도 장르의 맥락적 요소를 잘 표현해주고 있었으며, 패싯의 적용은 특정 방송내용에 대한 식별을 증진시킬 것으로 기대되었다.

신경망을 이용한 내용기반 영상 분류 (A Content-Based Image Classification using Neural Network)

  • 이재원;김상균
    • 한국멀티미디어학회논문지
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    • 제5권5호
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    • pp.505-514
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    • 2002
  • 본 논문에서는 내용기반 영상 분류를 위한 방법론으로써 신경망을 이용한 방법을 제안한다. 분류 대상 영상은 인터넷상의 다양한 영상들 중에서 전경과 배경의 구분이 있는 객체 영상이다. 전처리 과정에서 영역 분할을 이용하여 영상 내에서 배경을 제거하고 객체 영역을 추출한다. 분류를 위한 특징은 웨이블릿 변환 후 푸출된 형태 특징과 질감특징을 이용한다 추출된 특징 값들을 이용하여 영상들에 대한 학습패턴을 생성하고 신경망 분류기를 구성 한다. 신경망의 학습 알고리즘은 역전파 알고리즘을 사용한다. 가장 효과적인 질감특징을 선 택 하기 위한 실험에서는 대각 모멘트가 가장 높은 분류률을 보여 주었다. 배경을 제거 하고 대각 모멘트를 특징으로 사용하여 실험하였을 때, 30종류에서 각 10개씩 총 300개의 학습 데이터와300개의 테스트 데이터에 대하여 각각 72.3%와 67%의 정분류률을 보였다.

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Gender Classification of Low-Resolution Facial Image Based on Pixel Classifier Boosting

  • Ban, Kyu-Dae;Kim, Jaehong;Yoon, Hosub
    • ETRI Journal
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    • 제38권2호
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    • pp.347-355
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    • 2016
  • In face examinations, gender classification (GC) is one of several fundamental tasks. Recent literature on GC primarily utilizes datasets containing high-resolution images of faces captured in uncontrolled real-world settings. In contrast, there have been few efforts that focus on utilizing low-resolution images of faces in GC. We propose a GC method based on a pixel classifier boosting with modified census transform features. Experiments are conducted using large datasets, such as Labeled Faces in the Wild and The Images of Groups, and standard protocols of GC communities. Experimental results show that, despite using low-resolution facial images that have a 15-pixel inter-ocular distance, the proposed method records a higher classification rate compared to current state-of-the-art GC algorithms.

Domain Adaptation for Opinion Classification: A Self-Training Approach

  • Yu, Ning
    • Journal of Information Science Theory and Practice
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    • 제1권1호
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    • pp.10-26
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    • 2013
  • Domain transfer is a widely recognized problem for machine learning algorithms because models built upon one data domain generally do not perform well in another data domain. This is especially a challenge for tasks such as opinion classification, which often has to deal with insufficient quantities of labeled data. This study investigates the feasibility of self-training in dealing with the domain transfer problem in opinion classification via leveraging labeled data in non-target data domain(s) and unlabeled data in the target-domain. Specifically, self-training is evaluated for effectiveness in sparse data situations and feasibility for domain adaptation in opinion classification. Three types of Web content are tested: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. Findings of this study suggest that, when there are limited labeled data, self-training is a promising approach for opinion classification, although the contributions vary across data domains. Significant improvement was demonstrated for the most challenging data domain-the blogosphere-when a domain transfer-based self-training strategy was implemented.

The Basic Concepts Classification as a Bottom-Up Strategy for the Semantic Web

  • Szostak, Rick
    • International Journal of Knowledge Content Development & Technology
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    • 제4권1호
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    • pp.39-51
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    • 2014
  • The paper proposes that the Basic Concepts Classification (BCC) could serve as the controlled vocabulary for the Semantic Web. The BCC uses a synthetic approach among classes of things, relators, and properties. These are precisely the sort of concepts required by RDF triples. The BCC also addresses some of the syntactic needs of the Semantic Web. Others could be added to the BCC in a bottom-up process that carefully evaluates the costs, benefits, and best format for each rule considered.