• 제목/요약/키워드: Content-based Image Retrieval

검색결과 319건 처리시간 0.118초

Interactive Genetic Algorithm for Content-based Image Retrieval

  • Lee, Joo-Young;Cho, Sung-Bae
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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    • pp.479-484
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    • 1998
  • As technology in a computer hardware and software advances, efficient information retrieval from multimedia database gets highly demanded. Recently, it has been actively exploited to retrieve information based on the stored contents. However, most of the methods emphasize on the points which are far from human intuition or emotion. In order to overcome this shortcoming , this paper attempts to apply interactive genetic algorithm to content-based image retrieval. A preliminary result with subjective test shows the usefulness of this approach.

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Content Based Image Retrieval Using Combined Features of Shape, Color and Relevance Feedback

  • Mussarat, Yasmin;Muhammad, Sharif;Sajjad, Mohsin;Isma, Irum
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제7권12호
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    • pp.3149-3165
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    • 2013
  • Content based image retrieval is increasingly gaining popularity among image repository systems as images are a big source of digital communication and information sharing. Identification of image content is done through feature extraction which is the key operation for a successful content based image retrieval system. In this paper content based image retrieval system has been developed by adopting a strategy of combining multiple features of shape, color and relevance feedback. Shape is served as a primary operation to identify images whereas color and relevance feedback have been used as supporting features to make the system more efficient and accurate. Shape features are estimated through second derivative, least square polynomial and shapes coding methods. Color is estimated through max-min mean of neighborhood intensities. A new technique has been introduced for relevance feedback without bothering the user.

내용기반 이미지 검색을 위한 MPEG-7 우위컬러 기술자의 효과적인 유사도 (An Effective Similarity Measure for Content-Based Image Retrieval using MPEG-7 Dominant Color Descriptor)

  • 이종원;낭종호
    • 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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    • 제16권8호
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    • pp.837-841
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    • 2010
  • 본 논문에서는 MPEG-7 DCD를 이용하여 내용기반 이미지 검색을 할 때 적합한 유사도 측정 방법을 제안한다. 제안한 방법은 이미지에서 추출한 도미넌트 컬러의 비율에 따라 유사도를 측정할 수 있도록 하였다. 실험결과 제안한 방법은 MPEG-7 DCD의 QHDM[1]에 의한 검색결과보다 전역 DCD를 사용할 경우 ANMRR이 18.9%의 성능향상을 보였으며 블록별 DCD를 사용할 경우 47.2%라는 높은 성능향상을 보였다. 이는 제안한 방법이 DCD를 이용하여 내용기반 이미지 검색을 할 때 효과적인 유사도 측정 방법임을 보여준다. 특히, 영역 기반의 이미지 검색 방법에 유용하게 적용할 수 있을 것으로 보인다.

영상편집효과를 고려한 내용기반 영상 검색의 개선에 관한 연구 (Improvement of Content-based Image Retrieval by Considering Image Editing Effect)

  • 강석준;배태면;김기현;한승완;정치윤;남택용;노용만
    • 한국멀티미디어학회논문지
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    • 제9권5호
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    • pp.564-575
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    • 2006
  • 멀티미디어 컨텐츠가 급격히 증가함에 따라 사용자들은 다양한 유통 경로를 통하여 많은 멀티미디어 컨텐츠를 이용할 수 있게 되었다. 내용기반 영상 검색시스템은 영상 데이터의 내용을 다양한 시각적 특정 값들로 표현하여, 수많은 영상 중에서 사용자가 원하는 영상을 검색하고 원하지 않는 영상을 필터링 하도록 한다. 그러나 멀티미디어 데이터의 편집은 영상 데이터의 고유한 시각적 특정 값들을 왜곡시켜 잘못된 검색 결과나 필터링 결과를 제공하여 내용기반 영상 검색시스템의 성능을 저하시킨다. 본 논문에서는 이러한 영상편집효과 가운데 글자삽입, 프레임의 삽입, 그리고 여러 영상으로의 구성과 같은 편집효과에 대해 분석하고 이러한 편집효과를 제거하는 알고리즘을 고려한 내용기반 검색시스템을 제안하였으며, 실험을 통해 향상된 검색 결과를 확인할 수 있었다.

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Deep Hashing for Semi-supervised Content Based Image Retrieval

  • Bashir, Muhammad Khawar;Saleem, Yasir
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제12권8호
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    • pp.3790-3803
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    • 2018
  • Content-based image retrieval is an approach used to query images based on their semantics. Semantic based retrieval has its application in all fields including medicine, space, computing etc. Semantically generated binary hash codes can improve content-based image retrieval. These semantic labels / binary hash codes can be generated from unlabeled data using convolutional autoencoders. Proposed approach uses semi-supervised deep hashing with semantic learning and binary code generation by minimizing the objective function. Convolutional autoencoders are basis to extract semantic features due to its property of image generation from low level semantic representations. These representations of images are more effective than simple feature extraction and can preserve better semantic information. Proposed activation and loss functions helped to minimize classification error and produce better hash codes. Most widely used datasets have been used for verification of this approach that outperforms the existing methods.

영상 검색을 위한 적응적 컴포넌트 분석 시스템 설계 (The Design of Adaptive Component Analysis System for Image Retrieval)

  • 최철;박장춘
    • 한국컴퓨터정보학회지
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    • 제12권1호
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    • pp.9-19
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    • 2004
  • 본 논문에서는 내용 기반 영상 검색 시스템(Content Based Image Retrieval System)의 특징 추출(feature extraction)과 분석(analysis)을 위한 방법으로 적응적 컴포넌트 분석(ACA: Adaptive Component Analysis)을 제안하고 있다. 검색을 위해서 영상에서 추출된 특징들은 영상의 도메인(domain)에 따라 적절하게 적용해야만 좋은 검색 결과를 얻을 수 있다. 이러한 조건을 만족시키기 위한 방법으로 본 논문에서는 검색 측정도(retrieval measurement)를 제안하고 있다. ACA는 알고리즘과 시스템적인 관점에서 볼 때, 기존의 내용 기반 영상 검색을 위한 중간 단계라고 할 수 있으며, 검색 속도 향상 및 성능 개선에 목표를 두고 있다.

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Genetic Algorithm based Relevance Feedback for Content-based Image Retrieval

  • Seo, Kwang-Kyu
    • 반도체디스플레이기술학회지
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    • 제7권4호
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    • pp.13-18
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    • 2008
  • This paper explores a content-based image retrieval framework with relevance feedback based on genetic algorithm (GA). This framework adopts GA to learn the user preferences using the similarity functions defined for all available descriptors. The objective of the GA-based learning methods is to learn the user preferences using the similarity functions and to find a descriptor combination function that best represents the user perception. Experiments were performed to validate the proposed frameworks. The experiments employed the natural image databases and color and texture descriptors to represent the content of database images. The proposed frameworks were compared with the other two relevance feedback methods regarding effectiveness in image retrieval tasks. Experiment results demonstrate the superiority of the proposed method.

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내용기반 영상검색 시스템의 분석 및 발전 방안 (Anatomy of Current Issues on Content-Based Image Retrieval)

  • ;;박동원;안성옥
    • 컴퓨터교육학회논문지
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    • 제6권4호
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    • pp.31-36
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    • 2003
  • 내용기반 영상검색 분야에서의 활발한 연구로 지난 수년간 기술과 성능 면에서 괄목할 성장을 이룩해 내었다. 본 논문에서는 기존의 영상검색 시스템을 체계적으로 분석하여 아직까지 남아있는 취약점 및 개선 부분에 대하여 기술하였다. 특히, 의미론적 영상검색에 대하여 주안점을 두어 시스템 향상을 위하여 심도있게 연구가 진행 되어야 할 분야의 방향 및 주제를 분류하고 분석하여 제안하였다.

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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.

Content-Based Image Retrieval Using Multi-Resolution Multi-Direction Filtering-Based CLBP Texture Features and Color Autocorrelogram Features

  • Bu, Hee-Hyung;Kim, Nam-Chul;Yun, Byoung-Ju;Kim, Sung-Ho
    • Journal of Information Processing Systems
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    • 제16권4호
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    • pp.991-1000
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    • 2020
  • We propose a content-based image retrieval system that uses a combination of completed local binary pattern (CLBP) and color autocorrelogram. CLBP features are extracted on a multi-resolution multi-direction filtered domain of value component. Color autocorrelogram features are extracted in two dimensions of hue and saturation components. Experiment results revealed that the proposed method yields a lot of improvement when compared with the methods that use partial features employed in the proposed method. It is also superior to the conventional CLBP, the color autocorrelogram using R, G, and B components, and the multichannel decoded local binary pattern which is one of the latest methods.