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Image retrieval using DCT on row mean, column mean and both with image fragmentation

Published: 26 February 2010 Publication History

Abstract

How to search appropriate data from huge image pool has become vital issue. Because of easy availability of imaging devices, millions of images are being added to image pool every day. Image retrieval deals with searching relevant images from large image database. The paper presents novel image retrieval techniques based on discrete cosine transform applied on row mean, column mean and combination for feature extraction. Further the concept of image fragmentation is added to these to get total of 26 novel CBIR techniques. The proposed image retrieval techniques are applied to image database of 1000 images spread across 11 categories. Experimentation shows that taking row mean, column mean and combination improves the performance of image retrieval as compared to taking DCT of full image. Also fragmentation slightly helps in improving the image retrieval techniques.

References

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  • (2021)Image Retrieval using Weighted Fusion of GLCM and TSBTC Features2021 6th International Conference for Convergence in Technology (I2CT)10.1109/I2CT51068.2021.9418131(1-7)Online publication date: 2-Apr-2021
  • (2017)Color and texture fusion-based method for content-based Image Retrieval2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE)10.1109/ICCCCEE.2017.7867649(1-6)Online publication date: Jan-2017
  • (2017)DCT-SVD domain feature vector for image retrieval2017 International Conference on Signal Processing and Communication (ICSPC)10.1109/CSPC.2017.8305895(477-482)Online publication date: Jul-2017
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  1. Image retrieval using DCT on row mean, column mean and both with image fragmentation

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      cover image ACM Other conferences
      ICWET '10: Proceedings of the International Conference and Workshop on Emerging Trends in Technology
      February 2010
      1070 pages
      ISBN:9781605588124
      DOI:10.1145/1741906
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      • UNITECH: Unitech Engineers, India
      • AICTE: All India Council for Technical Education

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      Published: 26 February 2010

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      Author Tags

      1. CBIR
      2. DCT
      3. column mean
      4. fragmentation
      5. row mean

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      View all
      • (2021)Image Retrieval using Weighted Fusion of GLCM and TSBTC Features2021 6th International Conference for Convergence in Technology (I2CT)10.1109/I2CT51068.2021.9418131(1-7)Online publication date: 2-Apr-2021
      • (2017)Color and texture fusion-based method for content-based Image Retrieval2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE)10.1109/ICCCCEE.2017.7867649(1-6)Online publication date: Jan-2017
      • (2017)DCT-SVD domain feature vector for image retrieval2017 International Conference on Signal Processing and Communication (ICSPC)10.1109/CSPC.2017.8305895(477-482)Online publication date: Jul-2017
      • (2011)Speaker identification using row mean vector of spectrogramProceedings of the International Conference & Workshop on Emerging Trends in Technology10.1145/1980022.1980061(171-174)Online publication date: 25-Feb-2011
      • (2011)Performance evaluation of image retrieval using energy compaction and imagetiling over DCT row mean and DCT column meanThinkquest~201010.1007/978-81-8489-989-4_29(158-167)Online publication date: 2011

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