The International Arab Journal of Information Technology (IAJIT)

..............................
..............................
..............................


A Novel Image Retrieval Technique using Automatic and Interactive Segmentation

In this paper, we present a new region-based image retrieval technique based on robust image segmentation. Traditional content-based image retrieval deals with the global description of a query image. We combine the state-of-the-art segmentation algorithms with the traditional approach to narrow the area of interest to a specific region within a query image. In case of automatic segmentation, the algorithm divides a query image automatically and computes Zernike moments for each region. For interactive segmentation, our proposed scheme takes as input a query image and some information regarding the region of interest. The proposed scheme then works by computing the Geodesic-based segmentation of the query image. The segmented image is our region of interest which is then used for computing the Zernike moments. The Euclidean distance is then used to retrieve different relevant images. The experimental results clearly show that the proposed scheme works efficiently and produces excellent results.


[1] Amin A. and Deriche M., “Robust Image Segmentation Based on Convex Active Contours and the Chan Vese Model,” in Proceedings of IEEE Global Conference on Signal and Information Processing, Atlanta, pp. 1044-1048, 2014.

[2] Bai X. and Sapiro G., “Geodesic Matting: A Framework for Fast Interactive Image and Video Segmentation and Matting,” International Journal of Computer Vision, vol. 82, no. 2, pp. 113-132, 2009.

[3] Barnard K. and Shirahatti N., “Method for Comparing Content Based Image Retrieval Methods,” in Proceedings of Internet Imaging IV, Santa Clara, pp. 1-9, 2003.

[4] Chiang C., Hung Y., Yang H. and Lee G., “Region-Based Image Retrieval Using Color-Size Features of Watershed Regions,” Journal of Visual Communication and Image Representation, vol. 20, no. 3, pp. 167-177, 2009.

[5] Dalal N. and Triggs B., “Histograms of Oriented Gradients for Human Detection,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, pp. 886-893, 2005.

[6] Deriche M., Amin A., and Qureshi M., “Color Image Segmentation by Combining The Convex Active Contour and the Chan Vese Model,” Pattern Analysis and Applications, vol. 22, no. 2, pp. 1-15, 2017.

[7] Howarth P. and Ruger S., “Evaluation of Texture Features for Content-Based Image Retrieval,” in Proceedings of International Conference on Image and Video Retrieval, Dublin, pp. 326-334, 2004.

[8] Karuppusamy J. and Marappan K., “Efficient Color and Texture Feature Extraction Technique for Content Based Image Retrieval System,” The International Arab Journal of Information Technology, vol. 13, no. 6, pp. 784-790, 2016.

[9] Ke Y. and Sukthankar R., “PCA-SIFT: A more Distinctive Representation for Local Image Descriptors,” in Proceedings of IEEE conference on Computer Vision and Pattern Recognition, Washington, pp. 1-8, 2004.

[10] Kim W. and Kim Y., “A Region Based Shape Descriptor Using Zernike Moments,” Signal Processing: Image Communication, vol. 16, no. 1-2, pp. 95-102, 2000.

[11] Ledwich L. and Williams S., “Reduced Sift Features for Image Retrieval and Indoor Localisation,” in Proceedings of Australasian Conference on Robotics and Automation, Canberra, pp. 1-4, 2004.

[12] Lin K., Yang H., Hsiao J., and Chen C., “Deep Learning of Binary Hash Codes for Fast Image Retrieval,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, pp. 27-35, 2015.

[13] Lowe D., “Distinctive Image Features from Scale-Invariant Key Points,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.

[14] Maron O. and Ratan A., “Multiple-Instance Learning for Natural Scene Classification,” in Proceedings of the 15th International Conference on Machine Learning, San Francisco, pp. 341- 349, 1998.

[15] Mikolajczyk K. and Schmid C., “A Performance Evaluation of Local Descriptors,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615-1630, 2005.

[16] Peng B., Zhang L., and Zhang D., “Automatic Image Segmentation by Dynamic Region Merging,” IEEE Transactions on Image Processing, vol. 20, no. 12, pp. 3592-3605, 2011.

[17] Rahmani R., Goldman S., Zhang H., Krettek J., and Fritts J., “Localized Content-Based Image Retrieval,” in Proceedings of the 7th ACM SIGMM International Workshop on Multimedia 410 The International Arab Journal of Information Technology, Vol. 17, No. 3, May 2020 Information Retrieval, Hilton, pp. 227-236, 2005.

[18] Shanmugam B., Rathinavel R., Perumal T., and Subbaiyan S., “An Efficient Perceptual of CBIR System using MIL-SVM Classification and SURF Feature Extraction,” The International Arab Journal of Information Technology, vol. 14, no. 4, pp. 428-435, 2017.

[19] Shu X. and Wu X., “A Novel Contour Descriptor for 2D Shape Matching and its Application to Image Retrieval,” Image and Vision Computing, vol. 29, no. 4, pp. 286-294, 2011.

[20] Smeulders A. W., Worring M., Santini S., Gupta A., and Jain R., “Content-Based Image Retrieval at The End of The Early Years,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1349-1380, 2000.

[21] Wan J., Wang D., Hoi S., Wu P., Zhu J., Zhang Y., and Li J., “Deep learning for Content-Based Image Retrieval: A Comprehensive Study,” in Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, pp. 157-166, 2014.

[22] Wang J., Li J., and Wiederhold G., “SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 9, pp. 947-963, 2001.

[23] Yang C., Duraiswami R., Gumerov N., and Davis L., “Improved Fast Gauss Transform and Efficient Kernel Density Estimation,” in Proceedings of the 9th IEEE International Conference on Computer Vision, Nice, pp. 664- 671, 2003.

[24] Zhang D. and Lu G., “Shape-based Image Retrieval Using Generic Fourier Descriptor,” Signal Processing: Image Communication, vol. 17, no. 10, pp. 825-848, 2002.

[25] Zhang D. and Lu G., “A Comparative Study of Curvature Scale Space and Fourier Descriptors for Shape Based Image Retrieval,” Journal of Visual Communication and Image Representation, vol. 14, no. 1, pp. 39-57, 2003. Asjad Amin received the B.S. degree in telecommunication engineering from NU-FAST, Islamabad, Pakistan, M.S. degree in electrical engineering from University of Engineering & Technology, Taxila, Pakistan and Ph.D. in electrical engineering from King Fahd University of Petroleum & Minerals, Saudi Arabia in 2017. Since 2008, he is with the department of telecommunication engineering at The Islamia University of Bahawalpur, currently serving as Assistant Professor. His research interests include image and video processing, seismic imaging and modeling, machine learning, and image segmentation. Muhammad Qureshi is currently working as Assistant Professor in Telecommunication Engineering Department, University College of Engineering & Technology, The Islamia University of Bahawalpur. He received his B.Sc. degree in electrical engineering from UET Lahore in 2000 and M.Sc. in telecommunication engineering from NWFP- UET Peshawar in 2008. He completed his Ph.D. in Electrical Engineering from KFUPM, Saudi Arabia in 2017 His research interests include image & video processing, image compression, image forensics, and image quality assessment. He has published 18 refereed international journals and conference papers. Muhammad Ali Qureshi is a senior member of IEEE and reviewer of many renowned international journals with good impact factors in the related discipline.