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    RISS 인기검색어

      Efficient content-based image retrieval for multi-image queries

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      https://www.riss.kr/link?id=T14052200

      • 저자
      • 발행사항

        Seoul : Kyung Hee University, 2016

      • 학위논문사항
      • 발행연도

        2016

      • 작성언어

        영어

      • KDC

        005.74 판사항(6)

      • DDC

        005.74 판사항(23)

      • 발행국(도시)

        서울

      • 형태사항

        v, 34 leaves : illustrations ; 26 cm

      • 일반주기명

        Adviser: Young-Koo Lee
        Bibliography: leaves 33-34

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      다국어 초록 (Multilingual Abstract)

      With explosive growth of digital images, content-based image retrieval has been emerged as an active research topic with many opportunities and challenges. Most of the visual search systems focus on the task of finding specific object, but a little attention has been paid for multiple object retrieval. In this paper, we present an efficient framework for processing multi-image queries, where the users can input multiple images with their objects of interest. We first propose a number of methods to encode image representation of queries. This encoded representation can be indexed further to retrieve an initial list of candidates. Moreover, we present the measuring scheme using Geometric Verification to estimate the similarity between query and candidate images during re-ranking stage. The experimental results performed on a benchmark dataset show that our approach provides promising performance.
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      With explosive growth of digital images, content-based image retrieval has been emerged as an active research topic with many opportunities and challenges. Most of the visual search systems focus on the task of finding specific object, but a little at...

      With explosive growth of digital images, content-based image retrieval has been emerged as an active research topic with many opportunities and challenges. Most of the visual search systems focus on the task of finding specific object, but a little attention has been paid for multiple object retrieval. In this paper, we present an efficient framework for processing multi-image queries, where the users can input multiple images with their objects of interest. We first propose a number of methods to encode image representation of queries. This encoded representation can be indexed further to retrieve an initial list of candidates. Moreover, we present the measuring scheme using Geometric Verification to estimate the similarity between query and candidate images during re-ranking stage. The experimental results performed on a benchmark dataset show that our approach provides promising performance.

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      목차 (Table of Contents)

      • Abstract ---------------------------------------------------------------------v
      • 1. Introduction--------------------------------------------------------------1
      • 2. Related Works-----------------------------------------------------------4
      • 3. Background knowledge------------------------------------------------7
      • 3.1 Image retrieval system---------------------------------------------7
      • Abstract ---------------------------------------------------------------------v
      • 1. Introduction--------------------------------------------------------------1
      • 2. Related Works-----------------------------------------------------------4
      • 3. Background knowledge------------------------------------------------7
      • 3.1 Image retrieval system---------------------------------------------7
      • 3.2 Feature Detection---------------------------------------------------8
      • 4. Method using Distributed Frameworks for Image Database--9
      • 4.1 MapReduce design--------------------------------------------------9
      • 4.2 Spark design --------------------------------------------------------13
      • 5. Methodology-------------------------------------------------------------16
      • 5.1 Overview of Proposed Method ---------------------------------16
      • 5.2 Image Representation-----------------------------------------------17
      • 5.3 Image Description---------------------------------------------------18
      • 5.4 Scoring Scheme with Geometric Verification----------------27
      • 6. Experiments--------------------------------------------------------------29
      • 7. Conclusion---------------------------------------------------------------32
      • 8. References----------------------------------------------------------------33
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