• Title/Summary/Keyword: Similarity retrieval

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Content-Based Image Retrieval using Histogram Area Calculation (히스토그램 영역계산을 이용한 내용기반 영상검색)

  • Park, Min-Sheik;Yoo, Gi-Hyoung;Kwak, Hoon-Sung
    • Journal of the Korea Computer Industry Society
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    • v.6 no.2
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    • pp.265-270
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    • 2005
  • Histogram is very sensitive in lighting because of feature between color space. When it has intensity of moved light, It may be possibility that similarity drop down, So In this paper, introduce new image retrieval method that calls HAC (Histogram Area Calculation). This method divides area of Histogram by a few area and calculate areas. The proposed method is to calculate area of Histogram and compare similarity based on feature that histogram has presently. Performance of our proposed method was verified more excellent than other Conventional method and Merged Color Histogram.

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dynamic Information Ranking using Multiple Information filtering (다중 정보 여과 방법을 이용한 동적 정보 우선 순위 결정)

  • Kim, Jin;Yoon, Jeong-Seob;Jo, Genu-Sik
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2000.04a
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    • pp.323-332
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    • 2000
  • 인터넷을 등장으로, 끊임없이 늘어나는 정보의 양은 오히려 사용자의 정보 습득을 어렵게 만들었다. 이를 해결하기 위한 방법으로 검색된 정보에 우선 순위를 부여함으로써 사용자가 원하는 정보를 선별할 수 있는 방법이 등장하였다. 하지만, 이는 사용자의 일시적인 질의만을 가지고 정보의 우선 순위를 결정하기 때문에 사용자가 다시 판단해야 하는 부담을 안게 되었다. 이러한 문제점을 해결하기 위해, 본 논문에서는 내용 기반의 정보 검색(Content-Based Information Retrieval) 방법과 더불어 사용자의 기호를 반영하는 사용자 선호도 기반의 정보 여과(Information Filtering) 방법, 그룹 선호도 기반의 협동적 정보 여과(Collaborative Filtering) 방법을 사용하여 사용자의 요구에 선결조건으로 하며, 구축된 선호도는 벡터로써 표현되어 정보와의 유사도(degree of similarity) 계산에 사용된다. 제안된 방법을 실험하기 위해 MFC(Microsoft Foundation Class) 관련 학습 사이트를 구현하여 사용자 등록을 받았다. 이 과정에서 사용자에게 여러 가지 프로파일을 요구하였으며, 변화하는 사용자의 기호를 반영하기 위해 지속적으로 사용자의 행동을 관찰하였다. 이렇게 구축된 사용자 선호도를 바탕으로 제안된 방법을 실험하고 사용자의 feedback을 통해 결과에 대한 평가를 받아, 논문에서 제안된 방법의 타당성을 입증하였다.

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Content similarity matching for video sequence identification

  • Kim, Sang-Hyun
    • International Journal of Contents
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    • v.6 no.3
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    • pp.5-9
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    • 2010
  • To manage large database system with video, effective video indexing and retrieval are required. A large number of video retrieval algorithms have been presented for frame-wise user query or video content query, whereas a few video identification algorithms have been proposed for video sequence query. In this paper, we propose an effective video identification algorithm for video sequence query that employs the Cauchy function of histograms between successive frames and the modified Hausdorff distance. To effectively match the video sequences with a low computational load, we make use of the key frames extracted by the cumulative Cauchy function and compare the set of key frames using the modified Hausdorff distance. Experimental results with several color video sequences show that the proposed algorithm for video identification yields remarkably higher performance than conventional algorithms such as Euclidean metric, and directed divergence methods.

Ranking Tag Pairs for Music Recommendation Using Acoustic Similarity

  • Lee, Jaesung;Kim, Dae-Won
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.15 no.3
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    • pp.159-165
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    • 2015
  • The need for the recognition of music emotion has become apparent in many music information retrieval applications. In addition to the large pool of techniques that have already been developed in machine learning and data mining, various emerging applications have led to a wealth of newly proposed techniques. In the music information retrieval community, many studies and applications have concentrated on tag-based music recommendation. The limitation of music emotion tags is the ambiguity caused by a single music tag covering too many subcategories. To overcome this, multiple tags can be used simultaneously to specify music clips more precisely. In this paper, we propose a novel technique to rank the proper tag combinations based on the acoustic similarity of music clips.

Semantic Similarity-Based Contributable Task Identification for New Participating Developers

  • Kim, Jungil;Choi, Geunho;Lee, Eunjoo
    • Journal of information and communication convergence engineering
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    • v.16 no.4
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    • pp.228-234
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    • 2018
  • In software development, the quality of a product often depends on whether its developers can rapidly find and contribute to the proper tasks. Currently, the word data of projects to which newcomers have previously contributed are mainly utilized to find appropriate source files in an ongoing project. However, because of the vocabulary gap between software projects, the accuracy of source file identification based on information retrieval is not guaranteed. In this paper, we propose a novel source file identification method to reduce the vocabulary gap between software projects. The proposed method employs DBPedia Spotlight to identify proper source files based on semantic similarity between source files of software projects. In an experiment based on the Spring Framework project, we evaluate the accuracy of the proposed method in the identification of contributable source files. The experimental results show that the proposed approach can achieve better accuracy than the existing method based on comparison of word vocabularies.

Fuzzy based Thesaurus Construction Supporting Component Retrieval (컴포넌트 검색을 지원하는 퍼지 기반 시소러스 구축)

  • Kim, Gui-Jung;Han, Jung-Soo;Song, Young-Jae
    • The KIPS Transactions:PartD
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    • v.10D no.5
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    • pp.753-762
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    • 2003
  • Many Methodologies have proposed for component retrieval. Among them, thesaurus concept has introduced for similar component retrieval. This paper classified classes by concept according to inheritance relation for efficient retrieval of component, and applied fuzzy logic to thesaurus method and constructed object-oriented thesaurus. Proposed method could express category between concepts automatically, and calculate fuzzy degree between classes by comparing matching and mismatching degree to each class and category and construct thesaurus. Component retrieval is that using classes of component, candidate components are retrieved according to priority order using fuzzy similarity. Also, we improved retrieval performance by thesaurus greatly, setting critical of most suitable through simulation.

Mathematical Properties of the Formulas Evaluating Boolean Operators in Information Retrieval (정보검색에서 부울연산자를 연산하는 식의 수학적 특성)

  • 이준호;이기호;조영화
    • Journal of the Korean Society for information Management
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    • v.12 no.1
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    • pp.87-97
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    • 1995
  • Boolean retrieval systems have been most widely used in the area of information retrieval due to easy implementation and efficient retrieval. Conventional Boolean retrieval systems. however, cannot rank retrieved documents in decreasing order of query-document similarities because they cannot compute similarity coefficients between queries and documents. Extended Boolean models such as fuzzy set. Waller-Kraft, Paice, P-Norm and Infinite-One have been developed to provide the document ranking facility. In extended Boolean models, the formulas evaluating Boolean operators AND and OR are an important component to affect the quality of document ranking. In this paper we present mathematical properties of the formulas, and analyse their effect on retrieval effectiveness. Our analyses show that P-Norm is the most suitable for achieving high retrieval effectiveness.

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An Emotion-based Image Retrieval System by Using Fuzzy Integral with Relevance Feedback

  • Lee, Joon-Whoan;Zhang, Lei;Park, Eun-Jong
    • Proceedings of the IEEK Conference
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    • 2008.06a
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    • pp.683-688
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    • 2008
  • The emotional information processing is to simulate and recognize human sensibility, sensuality or emotion, to realize natural and harmonious human-machine interface. This paper proposes an emotion-based image retrieval method. In this method, user can choose a linguistic query among some emotional adjectives. Then the system shows some corresponding representative images that are pre-evaluated by experts. Again the user can select a representative one among the representative images to initiate traditional content-based image retrieval (CBIR). By this proposed method any CBIR can be easily expanded as emotion-based image retrieval. In CBIR of our system, we use several color and texture visual descriptors recommended by MPEG-7. We also propose a fuzzy similarity measure based on Choquet integral in the CBIR system. For the communication between system and user, a relevance feedback mechanism is used to represent human subjectivity in image retrieval. This can improve the performance of image retrieval, and also satisfy the user's individual preference.

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3D partial object retrieval using cumulative histogram (누적 히스토그램을 이용한 3차원 물체의 부재 검색)

  • Eun, Sung-Jong;Hyoen, Dae-Hwan;Lee, Ki-Jung;WhangBo, Taeg-Keun
    • 한국HCI학회:학술대회논문집
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    • 2009.02a
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    • pp.669-672
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    • 2009
  • The techniques extract shape descriptors from 3D models and use these descriptors for indices for comparing shape similarities. Most similarity search techniques focus on comparisons of each individual 3D model from databases. However, our similarity search technique can compare not only each individual 3D model, but also partial shape similarities. The partial shape matching technique extends the user's query request by finding similar parts of 3D models and finding 3D models which contain similar parts. We have implemented an experimental partial shape-matching search system for 3D pagoda models, and preliminary experiments show that the system successfully retrieves similar 3D model parts efficiently.

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Measurement of Document Similarity using Word and Word-Pair Frequencies (단어 및 단어쌍 별 빈도수를 이용한 문서간 유사도 측정)

  • 김혜숙;박상철;김수형
    • Proceedings of the IEEK Conference
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    • 2003.07d
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    • pp.1311-1314
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    • 2003
  • In this paper, we propose a method to measure document similarity. First, we have exploited single-term method that extracts nouns by using a lexical analyzer as a preprocessing step to match one index to one noun. In spite of irrelevance between documents, possibility of increasing document similarity is high with this method. For this reason, a term-phrase method has been reported. This method constructs co-occurrence between two words as an index to measure document similarity. In this paper, we tried another method that combine these two methods to compensate the problems in these two methods. Six types of features are extracted from two input documents, and they are fed into a neural network to calculate the final value of document similarity. Reliability of our method has been proved by an experiment of document retrieval.

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