An Efficient Content Based Image Retrieval System By Optimizing SOH

Authors

  • S Agalya  Computer Science Department, Pondicherry University, Puducherry, Tamil Nadu, India

Keywords:

Query Image(QI), k-Nearest Neighbor, Feature Extraction, SOH

Abstract

This work presents a Content-Based Image Retrieval (CBIR) system embedded with a clustering technique to retrieve images similar to query image. In this work, extensive robust and important features were extracted from the images database and then stored in the feature repository. This feature set is composed of color signature with the shape and color texture features. After that number of cluster formed in dataset. Cluster formation based on find Euclidean distance between each pairs in dataset QI image feature extraction is based on salient orientation histogram. Consequently, a novel image retrieval using k nearest neighbour (KNN) classifier is achieved between the features of the QI and the features of the cluster images. This method is entirely different from the existing histograms, most of the existing histogram techniques merely count the number or frequency of pixels. However, the unique characteristic of SOH is that they count the perceptually uniform color difference between two points under different backgrounds with regard to colors and edge orientations. Our proposed CBIR system is assessed by inquiring number of images (from the test dataset) and the efficiency of the system is evaluated by calculating precision-recall value for the results. The results were superior to other state-of-the-art CBIR systems in regard to precision

References

  1. E. Yildizer, A. M. Balci, M. Hassan, and R. Alhajj, “Efficient content-based image retrieval using Multiple Support Vector Machines Ensemble,” Expert Syst. Appl., vol. 39, no. 3, pp. 2385–2396, 2012.
  2. Y. Xu, J. Gong, L. Xiong, Z. Xu, J. Wang, and Y. qing Shi, “A privacy-preserving content-based image retrieval method in cloud environment,” J. Vis. Commun. Image Represent., vol. 43, pp. 164–172, 2017.
  3. S. Fadaei, R. Amirfattahi, and M. R. Ahmadzadeh, “Local derivative radial patterns: A new texture descriptor for content-based image retrieval,” Signal Processing, vol. 137, pp. 274–286, 2017.
  4. C. Höschl and J. Flusser, “Robust histogram-based image retrieval,” Pattern Recognit. Lett., vol. 69, pp. 72–81, 2016.
  5. A. Kumar et al., “Adapting content-based image retrieval techniques for the semantic annotation of medical images,” Comput. Med. Imaging Graph., vol. 49, pp. 37–45, 2016.
  6. J. Yue, Z. Li, L. Liu, and Z. Fu, “Content-based image retrieval using color and texture fused features,” Math. Comput. Model., vol. 54, no. 3–4, pp. 1121–1127, 2011.
  7. M. Mehrabi, F. Zargari, M. Ghanbari, and M. A. Shayegan, “Fast content access and retrieval of JPEG compressed images,” Signal Process. Image Commun., vol. 46, pp. 54–59, 2016.
  8. E. de Ves, X. Benavent, I. Coma, and G. Ayala, “A novel dynamic multi-model relevance feedback procedure for content-based image retrieval,” Neurocomputing, vol. 208, pp. 99–107, 2016.
  9. A. Alzu’bi, A. Amira, and N. Ramzan, “Content-based image retrieval with compact deep convolutional features,” Neurocomputing, vol. 249, pp. 95–105, 2017.
  10. D. C. G. Pedronette and R. da S. Torres, “Unsupervised rank diffusion for content-based image retrieval,” Neurocomputing, vol. 260, pp. 478–489, 2017.
  11. A. Manno-Kovacs, “CONTENT BASED IMAGE RETRIEVAL USING SALIENT ORIENTATION HISTOGRAMS Andrea Manno-Kovacs,” Image Process. (ICIP), 2016 IEEE Int. Conf., pp. 2481–2484, 2016.
  12. A. Manno-Kovacs, “CONTENT BASED IMAGE RETRIEVAL USING SALIENT ORIENTATION HISTOGRAMS Andrea Manno-Kovacs,” Image Process. (ICIP), 2016 IEEE Int. Conf., pp. 2481–2484, 2016.
  13. D. Feng, N. Barnes, and S. You, “HOSO?: Histogram Of Surface Orientation for RGB-D Salient Object Detection.”
  14. J. Zhang, S. Feng, D. Li, Y. Gao, Z. Chen, and Y. Yuan, “Image retrieval using the extended salient region,” Inf. Sci. (Ny)., vol. 399, pp. 1339–1351, 2017.
  15. S. Feng, D. Xu, and X. Yang, “Attention-driven salient edge(s) and region(s) extraction with application to CBIR,” Signal Processing, vol. 90, no. 1, pp. 1–15, 2010.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2018-04-30

Issue

Volume 4, Issue 4, March-April-2018

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
S Agalya, " An Efficient Content Based Image Retrieval System By Optimizing SOH , International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 4, pp.1178-1183, March-April-2018.