IMPLEMENTING SKETCH-BASED IMAGE RETRIEVAL SYSTEM USING EDGE DETECTION AND SIFT ALGORITHM | ||
| Journal of University of Anbar for Pure Science | ||
| Article 8, Volume 13, Issue 2, August 2019, Pages 55-63 PDF (660.99 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.37652/juaps.2022.172129 | ||
| Author | ||
| AHMED SHIHAB AHMED* | ||
| Department of Basic Sciences, College of Nursing, University of Baghdad, Baghdad, Iraq | ||
| Abstract | ||
| Throughout the past few years, several researchers have introduced various methods and various algorithms for a precise and dependable sketch-based image retrieval system. In this paper, a proposed sketch-based image retrieval system is introduced. The framework goes over two phases: creating the sketch dataset phase and implementing SIFT (scale invariant feature transform) algorithm. The sketch dataset was created by selecting 100 colored image passed through canny edge detection operator. The system tends to enter a line-based/hand-drawing sketch and applies the SIFT algorithm to match between the input sketch and all sketches in the dataset. SIFT is one of the main efficient algorithms that are used to make description and matching, since it works on large keypoints. This system retrieves images depending on sketch image, and the result of matching will retrieve images that are approximate the entered sketch. The proposed system is assessed according to the measures that are utilized in detection, description and matching grounds, which are precision, recall and accuracy measures. The system showed (96 %) accuracy for line based sketches and (84%) for hand drawing where the detection was identical. | ||
| Keywords | ||
| Sketch-Based Image Retrieval; Edge Detection; SIFT; Keypoints; Based-Line Sketch; Hand Drawing Sketch | ||
| References | ||
|
[1] R. Deepu, M. Goyal, ”A Research Paper on Content Based Image Retrieval System using Improved SVM Technique”, International Journal Of Engineering And Computer Science, Vol. 3, Issue 12 , pp. 9755-9760, 2014.
[2] J. Yue, Z. Li, L. Liu and Z. Fu, “Content-based image retrieval using color and texture fused features”, Mathematical and Computer Modelling, Vol. 54, Issues 3, pp.1121-1127, 2011.
[3] M .Inoue, ”Image retrieval: Research and use in the information theory”, Special issue: Leading ICT technologies in the Information Explosion, Progress in information, No.6, pp.3-4, 2009.
[4] F. Pengand, A. McCallum, “Information extraction from research papers using conditional random fields”, Information Processing & Management, Vol. 42, Issue 4, pp.963-979, 2006.
[5] R. Ethan, V. Rabaud, K. Konolige, G. R. Bradski. ”ORB: An efficient alternative to SIFT or SURF,” IEEE International Conference on Computer Vision, vol. 11, No. 1, pp. 2, 2011.
[6] L.Bin, M.S. Yeganeh,”Comparison for Image Edge Detection Algorithms,” IOSR Journal of Computer Engineering, Vol. 2, Issue 6, pp.1-4, 2012.
[7] R. Hua, J. Collomossea, ”A Performance Evaluation of Gradient Field HOG Descriptor for Sketch Based Image Retrieval”, Computer Vision and Image Understanding, Vol. 117, Issue 7, pp.790-806, 2013.
[8] A. Sravanthi, B. H.Reddy, ”Sketch4Match – Content- based Image Retrieval System Using Sketches”, International Journal of Engineering Research & Technology (IJERT),Vol. 1, Issue 7, pp. 1-6, 2012.
[9] A.I. Doush, A.B. Sahar,”Currency recognition using a smartphone: Comparison between color SIFT and gray scale SIFT algorithms,” Journal of King Saud University – Computer and Information Sciences, Vol. 29, No. 4, pp. 484-492, 2017.
[10] H. A. Abdulbaqi, G. Sulong and S. H. Hashem,”A Sketch Based Image Retrieval: A Review of Literature,” Journal of Theoretical and Applied Information Technology, Vol. 63, No.1, pp.158-167, 2014.
[11] M. Nosrati, R.Karimi, M.Hariri and K. Malekian,”Edge Detection Techniques in Processing Digital Images: Investigation of Canny Algorithm and Gabor Method,” World Applied Programming, Vol. 3, Issue 3, pp.116-121, 2013.
[12] S. Vijayarani, M. Vinupriya,”Performance Analysis of Canny and Sobel Edge Detection Algorithms in Image Mining,” International Journal of Innovative Research in Computer and Communication Engineering, Vol. 1, Issue 8, pp. 1760-1767, 2013.
[13] R. Mehra, L. Sharma and N.sha, “Comparative Analysis of Canny and Prewitt Edge Detection Techniques used in Image Processing”, International Journal of Engineering Trends and Technology (IJETT), Vol. 28 No.1, pp. 48-53, 2015.
[14] R. Jindal, S. Vatta,”SIFT: Scale Invariant Feature Transform (Review)”, International Journal of Advance research, Ideas and Innovations in Technology, Vol. 1, Issue 1, pp.1-5, 2014.
[15] D. G. Lowe,”Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, Vol. 60, No.2, pp. 91-110, 2004.
[16] T. Serre, M. Kouh, C. Cadieu , U. Knoblich, G. Kreiman and T. Poggio, ”A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex”, Computer Science and Artificial Intelligence Laboratory Technical, pp. 1-130, 2005.
[17] Trupti P. Patel, Sandip R. Panchal,” Corner Detection Techniques: An Introductory Survey”, Journal of IJEDR, Volume 2, Issue 4, pp. 3680-3686, 2014.
[18] Z. Li, H. Jia, Y. Zhang, S. Liu and H. Zhang, “Efficient parallel optimizations of a high-performance SIFT on GPUs”, Journal of Parallel and Distributed Computing, Vol. 124, pp.78-91, 2019.
[19] V. Renò, G. Dimauro, G. Labate, E. Stella, R. Maglietta, “A SIFT-based software system for the photo-identification of the Risso's dolphin”, Journal of Ecological Informatics, Vol. 50, pp.95-101, 2019.
[20] M. R. Daliri, “Automated Diagnosis of Alzheimer Disease using the Scale-Invariant Feature Transforms in Magnetic Resonance Images”, Journal of Medical Systems, Vol. 36, Issue 2, pp. 995–1000, 2012.
[21] Y. Cheng-bo, L. Yu-xuan, Z. Jing and Y.Ting, “Object tracking in the complex environment based on SIFT”, in IEEE 3rd International Conference on Communication Software and Networks, China, May 27-29, 2011, pp. 150-153. | ||
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