Supporting Content-Based Retrieval in Large Image Database Systems

被引:0
|
作者
Edward Remias
Gholamhosein Sheikholeslami
Aidong Zhang
Tanveer Fathima Syeda-Mahmood
机构
[1] State University of New York at Buffalo,Department of Electrical and Computer Engineering
[2] State University of New York at Buffalo,Department of Computer Science
[3] Xerox Research Center,undefined
来源
关键词
content-based image retrieval; image database systems; texture; image decomposition; image representation; wavelet transforms;
D O I
暂无
中图分类号
学科分类号
摘要
In this paper, we investigate approaches to supporting effective and efficient retrieval of image data based on content. We firstintroduce an effective block-oriented image decomposition structure which can be used to represent image content inimage database systems. We then discuss theapplication of this image data model to content-based image retrieval.Using wavelet transforms to extract image features,significant content features can be extracted from image datathrough decorrelating the data in their pixel format into frequency domain. Feature vectors ofimages can then be constructed. Content-based image retrievalis performed by comparing the feature vectors of the query imageand the decomposed segments in database images.Our experimental analysis illustrates that the proposed block-oriented image representationoffers a novel decomposition structure to be used tofacilitate effective and efficient image retrieval.
引用
收藏
页码:153 / 170
页数:17
相关论文
共 50 条
  • [41] Content-based retrieval and data mining of a skin cancer image database
    Chung, SM
    Wang, Q
    INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY: CODING AND COMPUTING, PROCEEDINGS, 2001, : 611 - 615
  • [42] Content-Based retrieval supporting similarity query
    Yoon, MH
    Kim, KC
    Yoon, YI
    INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED PROCESSING TECHNIQUES AND APPLICATIONS, VOLS I-V, PROCEEDINGS, 1999, : 218 - 224
  • [43] Content-based Image Retrieval for Medical Image
    Zheng, Kaimei
    2015 11TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS), 2015, : 219 - 222
  • [44] Content-Based Image Retrieval with LIRe and SURF on a Smartphone-Based Product Image Database
    Chen, Kai
    Hennebert, Jean
    PATTERN RECOGNITION, MCPR 2014, 2014, 8495 : 231 - +
  • [45] Integrating color and spatial information for content-based image retrieval inlarge image database
    Song, QB
    Yang, XR
    Shen, JY
    Chen, LM
    2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS, 2002, : 2082 - 2086
  • [46] A method of content-based image retrieval for a spinal x-ray image database
    Krainak, DM
    Long, LR
    Thoma, GR
    MEDICAL IMAGING 2002: PACS AND INTEGRATED MEDICAL INFORMATION SYSTEMS: DESIGN AND EVALUATION, 2002, 4685 : 108 - 116
  • [47] An approach for content-based image retrieval using region features in image database system
    Shen, JQ
    Geng, ZF
    ICEMI 2005: Conference Proceedings of the Seventh International Conference on Electronic Measurement & Instruments, Vol 6, 2005, : 437 - 441
  • [48] A method for evaluating the performance of content-based image retrieval systems
    Black, JA
    FIFTH IEEE SOUTHWEST SYMPOSIUM ON IMAGE ANALYSIS AND INTERPRETATION, PROCEEDINGS, 2002, : 96 - 100
  • [49] Content-based image retrieval in picture archiving and communications systems
    Qi, HR
    Snyder, WE
    JOURNAL OF DIGITAL IMAGING, 1999, 12 (02) : 81 - 83
  • [50] About Segmentation Step in Content-based Image Retrieval Systems
    Da Rugna, Jerome
    Chareyron, Gael
    Konik, Hubert
    WORLD CONGRESS ON ENGINEERING AND COMPUTER SCIENCE, WCECS 2011, VOL I, 2011, : 550 - 554