Matching evaluation based on image content discriminative features for different image types

ABSTRACTSearch by image has taken the place of the prior wordy approach, considering spatial image comparison in the feature space. Several query image forms are available. Sketch-to-real image matching is very challenging problem, which is commonly based on edge feature detectors to extract features from sketches. However, they are inconvenient, and it is crucial to select different methods. Thus, methods assessment is conducted for similarity matching between sketched and real images for different drawing contents and clarity levels. The assessment approach includes comparing feature keypoints gathered using the Scale-Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) methods. In addition, the comparison includes matching of objects inside High-Deﬁnition (HD) images and 3D images based on their generated features. The textual-Hcontent image search is another search type that is frequently used in brand logo cases. Hence, it is presented for text-logo image search based on visual features. Besides, the motives and downsides of the considered methods are declared, including the space and time complexities within test cases.KEYWORDS: SIFTORBsketch-real image retrievalobjects matchingspatial distance3D image matching Disclosure statementNo potential conflict of interests was reported by the author(s).Additional informationNotes on contributorsEman S. SabryEman S. Sabry was born in Cairo, Egypt. She received the B.Sc. in Electronics and Electrical Communications Engineering in 2007 with honors from Higher Institute of Engineering in El Shorouk Academy. She received the M.Sc. degree from the Faculty of Engineering of Arab Academy for Science, Technology and Maritime Transport in 2016. Currently, she is working as a teaching assistant at the Higher Institute of Engineering, El Shorouk Academy. Her current research interests are in artiﬁcial intelligence, deep learning, generative adversarial networks (GAN), vision transformer, capsule networks, data analysis, image processing, and retrieval. She has competence utilising Python to create current deep learning models across a variety of phases and applications. She also has an experience in communication systems, microwave engineering, antennas, optical signal processing, electronics, image and video compression, cloud computing, multimedia processing, and other areas. She also has an experience in developing and processing IP networks, such as IPTV, using OPNET and various network simulators.Salah ElagoozSalah Elagooz is the head of the Department of Communications and Computers Engineering at the Higher Institute of Engineering, El-Shorouk Academy. He received a bachelor's degree in electrical engineering with a specialization in “Telecommunications” from the Military Technical College of Egypt in July 1981. He received his master degree in electrical engineering in January 1988 from the Military Technical College also. He received his Ph.D. in electrical engineering from George Washington University in September 1993. He had the degree of assistant professor in electrical engineering in September 2000. He had the professor degree in electrical engineering in March 2010.Fathi E. Abd El-SamieFathi E. Abd El-Samie received the B.Sc. (Honors), M.Sc., and Ph.D. from the Faculty of Electronic Engineering, Menouﬁa University, Menouf, Egypt, in 1998, 2001, and 2005, respectively. He joined the teaching staff of the Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menouﬁa University, Menouf, Egypt, in 2005. He has received the most cited paper award from Digital Signal Processing journal for 2008. His current research areas of interest include image enhancement, image restoration, image interpolation, super-resolution reconstruction of images, data hiding, multimedia communications, medical image processing, optical signal processing, and digital communications.Nirmeen A. El-BahnasawyNirmeen A. El-Bahnasawy received her B.S. in Electronic Engineering in 1998 and M.Sc. and Ph.D. degrees in Computer Science and Engineering from Menouﬁa University in 2003 and 2013, respectively. She has been appointed as an Associate Professor at Menouﬁa University in 2019. Her research interests include distributed computing, grid computing, IoT, artiﬁcial intelligence, fog computing, and cloud computing. She has a deep experience in dealing with electronics H/W kits, different software tools, and different programming languages.Ghada M. El-BanbyGhada M. El-Banby received the M.Sc. and Ph.D. degrees in Automatic Control Engineering from Menouﬁa University, Egypt in 2006, and 2012, respectively. Currently, she is working as an Associative Professor at the Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menouﬁa University. Her current research interests include computer vision, data fusion, image processing, signal processing, medical imaging, modelling, and control.Rabie A. RamadanRabie A. Ramadan is one of the pioneers in the ﬁelds of Artiﬁcial Intelligence (AI) and sensing technologies in Egypt. He graduated from Alazhar University and got a master degree from both Cairo University and Southern Methodist University (SMU), Dallas, Texas, USA. He also received his Ph.D. from Computer Science and Engineering Department, Southern Methodist University (SMU), Dallas, Texas, USA.