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Image Indexing Using Color Correlograms

Published: 17 June 1997 Publication History

Abstract

We define a new image feature called the color correlogram and use it for image indexing and comparison. This feature distills the spatial correlation of colors, and is both effective and inexpensive for content-based image retrieval. The correlogram robustly tolerates large changes in appearance and shape caused by changes in viewing positions, camera zooms, etc. Experimental evidence suggests that this new feature outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval.

Cited By

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  • (2023)Coloration Parsing and Transfer Design of Han Folk CostumesJournal on Computing and Cultural Heritage 10.1145/358608216:4(1-17)Online publication date: 9-Aug-2023
  • (2021)It Takes Two to TangoProceedings of the 43rd International Conference on Software Engineering10.1109/ICSE43902.2021.00091(957-969)Online publication date: 22-May-2021
  • (2019)Learning Discriminative Features for Image RetrievalProceedings of the 2019 on International Conference on Multimedia Retrieval10.1145/3323873.3325032(96-104)Online publication date: 5-Jun-2019
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  1. Image Indexing Using Color Correlograms
      Index terms have been assigned to the content through auto-classification.

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      Information & Contributors

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      Published In

      cover image Guide Proceedings
      CVPR '97: Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
      June 1997
      ISBN:0818678224

      Publisher

      IEEE Computer Society

      United States

      Publication History

      Published: 17 June 1997

      Author Tags

      1. color histograms
      2. content-based image retrieval
      3. correlograms
      4. image indexing

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      Cited By

      View all
      • (2023)Coloration Parsing and Transfer Design of Han Folk CostumesJournal on Computing and Cultural Heritage 10.1145/358608216:4(1-17)Online publication date: 9-Aug-2023
      • (2021)It Takes Two to TangoProceedings of the 43rd International Conference on Software Engineering10.1109/ICSE43902.2021.00091(957-969)Online publication date: 22-May-2021
      • (2019)Learning Discriminative Features for Image RetrievalProceedings of the 2019 on International Conference on Multimedia Retrieval10.1145/3323873.3325032(96-104)Online publication date: 5-Jun-2019
      • (2019)An Unsupervised Genetic Algorithm Framework for Rank Selection and Fusion on Image RetrievalProceedings of the 2019 on International Conference on Multimedia Retrieval10.1145/3323873.3325022(58-62)Online publication date: 5-Jun-2019
      • (2019)New effective techniques for automatic detection and classification of external olive fruits defects based on image processing techniquesMultidimensional Systems and Signal Processing10.1007/s11045-018-0573-530:2(571-589)Online publication date: 1-Apr-2019
      • (2019)Principal texture direction based block level image reordering and use of color edge features for application of object based image retrievalMultimedia Tools and Applications10.1007/s11042-018-6246-478:2(1685-1717)Online publication date: 1-Jan-2019
      • (2019)Square texton histogram features for image retrievalMultimedia Tools and Applications10.1007/s11042-018-5795-x78:3(2719-2746)Online publication date: 1-Feb-2019
      • (2019)Partition selection with sparse autoencoders for content based image classificationNeural Computing and Applications10.1007/s00521-017-3099-031:3(675-690)Online publication date: 1-Mar-2019
      • (2018)Texture description using multi-scale morphological GLCMMultimedia Tools and Applications10.5555/3288443.328849577:23(30505-30532)Online publication date: 1-Dec-2018
      • (2018)Binary convolutional neural network features off-the-shelf for image to video linking in endoscopic multimedia databasesMultimedia Tools and Applications10.5555/3287850.328789677:21(28817-28842)Online publication date: 1-Nov-2018
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