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22 April 2024
Reseach Article

Improvement CBIR Performance of Region-based Segmentation on DCT Images

by Suhendro Y. Irianto, Sri Karnila, Dona Yuliawati
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 20
Year of Publication: 2022
Authors: Suhendro Y. Irianto, Sri Karnila, Dona Yuliawati
10.5120/ijca2022922220

Suhendro Y. Irianto, Sri Karnila, Dona Yuliawati . Improvement CBIR Performance of Region-based Segmentation on DCT Images. International Journal of Computer Applications. 184, 20 ( Jul 2022), 24-29. DOI=10.5120/ijca2022922220

@article{ 10.5120/ijca2022922220,
author = { Suhendro Y. Irianto, Sri Karnila, Dona Yuliawati },
title = { Improvement CBIR Performance of Region-based Segmentation on DCT Images },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2022 },
volume = { 184 },
number = { 20 },
month = { Jul },
year = { 2022 },
issn = { 0975-8887 },
pages = { 24-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number20/32434-2022922220/ },
doi = { 10.5120/ijca2022922220 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:21:58.370063+05:30
%A Suhendro Y. Irianto
%A Sri Karnila
%A Dona Yuliawati
%T Improvement CBIR Performance of Region-based Segmentation on DCT Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 20
%P 24-29
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In In this paper, we tried to discover the JPEG image which is presently an worldwide normal for pictures on the internet. Furthermost of the images presently on the storage media used regularly or on the net are in JPEG format. This JPEG format has some benefits compared to others, one of the benefit of JPEG is the size compared to others non JPEG data or information, so JPEG has an significant role in saving capacity much quality of the image. We used more than 50,000 natural images collected from the internet and other sources. Even though JPEG image has some advantages compared to others format, some researches still continue to find methos to improve CBIR performance on images, particularly on natural images. This is a reason why our research was carried out, in this work we applied region-based segmentation to improve the performance of image searching , both accurately and effectivity. We applied histogram based segmentation on natural images before deploying content based image retrieval. The evidence seems to indicate that the split and merge segmentation on JPEG image for image retrieval demonstrates a higher precision than on RGB images Even though the precision is not radically different, the Split and Merge approach can be used as an alternative technique to improve the effectiveness of image retrieval, particularly for DCT based images. Statistically, it also can be concluded that if the number of regions generated during segmentation is high, the precision tends to be higher. In the near future this trend could be considered for larger database with greater varieties of image category in order to get more accurate results.

References
  1. Y. Dong, “Image Classification in JPEG Compression Domain for Malaria Infection Detection,” 2022.
  2. M. Image and T. Ccl, “No Title.”
  3. A. Kumar, “Automatic Feature Weight Determination using Indexing and Pseudo Relevance Feedback for Multi-feature Content Based Image Retrieval,” pp. 1–9, 2018.
  4. I. M. Hameed, S. H. Abdulhussain, B. M. Mahmmod, I. M. Hameed, S. H. Abdulhussain, and B. M. Mahmmod, “Content-based image retrieval : A review of recent trends COMPUTER SCIENCE | REVIEW ARTICLE Content-based image retrieval : A review of recent trends,” Cogent Eng., vol. 8, no. 1, 2021, doi: 10.1080/23311916.2021.1927469.
  5. N. P. Kumar, “Content Based Image Retrieval Using Shape , Color and Texture,” vol. 7, no. 5, pp. 1151–1158, 2018, doi: 10.21275/ART20182457.
  6. M. Lachaize et al., “Evidential split-and-merge : Application to object-based image analysis To cite this version : HAL Id : hal-01918408 Evidential Split-and-Merge : Application to Object-Based,” 2018.
  7. R. Aslanzadeh, “An Efficient Evolutionary Based Method For Image Segmentation,” pp. 1–17.
  8. C. Ipn, “segmentation in fluorescence microscopy images,” 2021, doi: 10.1016/j.bspc.2019.101575.Split.
  9. O. A. Adegbola et al., “Modified one-class support vector machine for content-based image retrieval with relevance feedback Modified one-class support vector machine for content-based image retrieval with relevance feedback,” Cogent Eng., vol. 5, no. 1, pp. 1–20, 2018, doi: 10.1080/23311916.2018.1541702.
  10. I. O. F. S. Segmentation, “Markku Hautakoski Helsinki University of Technology Institute of Photogrammetry and Remote Sensing Helsinki, Finland,” pp. 32–39.
  11. T. Zhang, G. Lin, W. Liu, J. Cai, and A. Kot, “Splitting vs . Merging : Mining Object Regions with Discrepancy and Intersection Loss for Weakly Supervised Semantic Segmentation,” pp. 1–17.
  12. G. R. Jothilakshmi, P. Sharmila, and A. Raaza, “Mammogram Segmentation using Region based Method with Split and Merge Technique,” vol. 9, no. October, 2016, doi: 10.17485/ijst/2016/v9i40/99589.
  13. C. T. Rueden et al., “ImageJ2 : ImageJ for the next generation of scientific image data,” pp. 1–26, 2017, doi: 10.1186/s12859-017-1934-z.
  14. D. Adlakha, D. Adlakha, and R. Tanwar, “Analytical Comparison between Sobel and Prewitt Edge Detection Techniques,” vol. 7, no. 1, pp. 1482–1485, 2016.
  15. L. Gatos, “Split-and-Merge Segmentation of Aerial Photographs,” vol. 6, 1988.
  16. S. K. Katiyar, “Comparative analysis of common edge detection techniques in context of object extraction,” vol. 50, no. 11, pp. 68–79.
  17. B. Shikha, P. Gitanjali, and D. P. Kumar, “An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System,” pp. 15–27, 2020, doi: 10.9781/ijimai.2020.01.002.
  18. K. Porkaew, S. Mehrotra, M. Ortega, and K. Chakrabarti, “Similarity Search Using Multiple Examples in,” pp. 68–75, 1999.
  19. T. Gevers and A. W. M. Smeulders, “PicToSeek : Combining Color and Shape Invariant Features for Image Retrieval,” no. January 2000, 2014.
  20. Z. Li, O. R. Za, B. Yan, and C. Va, “C-BIRD : Content-Based Image Retrieval from Digital Libraries Using Illumination Invariance and Recognition Kernel C-BIRD : Content-Based Image Retrieval from Digital Libraries Using Illumination Invariance and Recognition Kernel,” no. March 1998, 2015, doi: 10.1109/DEXA.1998.707425.
  21. E. R. Andersson, R. Sandberg, and U. Lendahl, “Notch signaling : simplicity in design , versatility in function,” no. September, 2011, doi: 10.1242/dev.063610.
  22. S. Blue, “C OMBINING S TRUCTURE , C OLOR AND T EXTURE FOR I MAGE R ETRIEVAL : A P ERFORMANCE E VALUATION Qasim Iqbal and J . K . Aggarwal Computer and Vision Research Center Department of Electrical and Computer Engineering The University of Texas at Austin Austin , Texas 78712 , USA,” vol. 2, pp. 438–443, 2002.
  23. S. Eickeler, “High quality face recognition in JPEG compressed images,” no. February 1999, 2014, doi: 10.1109/ICIP.1999.821721.
Index Terms

Computer Science
Information Sciences

Keywords

Keywords: CBIR region segmentation DCT JPEG