Document Type : Research Paper

Author

Department of Computer Science, University Of Technology, Baghdad, Iraq

Abstract

For the reason of colossal technological developments, the requirement of image information methods became a significant issue. The aim of this research was to retrieve the word based on Fast Retina Key-points (FREAK) descriptor .The suggested system consists of four stages. In the first stage, the images of English letters are loaded. Points are detected via SUSAN in the second stage. FREAK used in the third stage and then a database was created containing 26 English letters. The image to be tested was entered and the points are extracted in the fourth stage and then Manhattan distance was used to calculate the distance between the value of the test image descriptors and all the values of the descriptors in a database. The experimental results show that the precision and the recall values were high for retrieval of the words when using SUSAN because it extracts a large number of interest points compared to the Harris method. For example, for the letter H was 104 with SUSAN while it was 42 for Harris, therefore; the precision for retrieval of the word Hour was 89% and recall was 93% when using SUSAN while precision was 77% and recall was 80% when using Harris.

Keywords

[1] P. Srivastava and A. Khare, "Content-based Image Retrieval using Scale Invariant Feature Transform and moments," IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electrical Engineering (UPCON), 162-166, 2016.
[2] S. Mahammadi and N. Mahesh, "Image Mosaic Using FAST Corner Detection, " International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE), Vol. 1, No. 6, pp. 1-6, December, 2012.
[3] C.H. Gomez, K. Medathati. P.Kornprobst, V. Murino, and D. Sona, "Improving FREAK Descriptor for Image Classification," in International Conference on Computer Vision, Copenhagen, Denmark, pp.14-23,July 6-9, 2015.
[4] A. Alahi, R. Ortiz and P. Vandergheynst, "FREAK: fast retina keypoint", IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, pp. 510–517, June, 2012.
[5] O. Guclu and B. Ahmet, "A Comparison of Feature Detectors and Descriptors in RGB-D SLAM Methods," Image Analysis and Recognition, Computer Society Conference on Computer Vision and Pattern Recognition, Ankara/Turkey, pp. 297-305, July, 2015.
[6] E. Tola ,V. Lpetit and P. Fau, "Daisy: An efficient dense descriptor applied to wide-baseline stero," IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 32, No. 5, pp.815-830, May, 2009.
[7] J.,Wang, X.,Wang, X. Yang and A. Zhao, "CS-FREAK: an improved binary descriptor,"in Chinese Conference on Image and Graphics Technologies, Beijing, China, pp.129-136, June, 2014.
[8] C. Whiten, R. Laganiere, and G.-A. Bilodeau, "Efficient action recognition with MoFREAK," International Conference on Computer and Robot Vision (CRV), Ottawa, Canada, pp. 319–325, May , 2013.
[9] M. Ami, "A Survey on Object Based Image Retrieval using Local and Global Features," International Journal Of Engineering And Computer Science, ISSN:2319-7242, Vol. 3, No. 10, pp. 8643-8646, December, 2014.
[10] N. Alyuz, B. Gokberk, and L. Akarun, “3-d face recognition under occlusion using masked projection,” IEEE Transactions on Information Forensics and Security, vol. 8, no. 5, pp. 789–802, May, 2013
[11] D. -D. Nguyen, A. El Ouardi, E. Aldea, and S. Bouaziz, "HOOFR: An Enhanced Bio-Inspired Feature Extractor, " 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico, pp.2977-2982, December, 2016.
[12] A.A. Karim and E.F. Nasser, "Image Retrieval from Video Streams Databases using Similarity of Clustering Histogram, " Al-Mansour Journal, No.29, pp.1-22, December, 2018.
[13] A.A. Karim and E. F. Nasser, "Improvement of Corner Detection Algorithms (Harris, FAST and SUSAN) Based on Reduction of Features Space and Complexity Time," Engineering & Technology Journal, Vol. 35, No. 2,Part B., pp.112-118, 2017.
[14] M. Alkhawlani, M.Elmogy, and H. Elbakry, "Content-based Image Retrieval using local Features descriptors and Bag-of-Visual Words," (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 6, No. 9, pp.212-219, 2015.