TY - GEN
T1 - Photobombing Removal Benchmarking
AU - Prakya, Sai Pavan Kumar
AU - Sainath, Madamanchi Manju Venkata
AU - Patel, Vatsa S.
AU - Baraheem, Samah Saeed
AU - Nguyen, Tam V.
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Photobombing occurs very often in photography. This causes inconvenience to the main person(s) in the photos. Therefore, there is a legitimate need to remove the photobombing from taken images to produce a pleasing image. In this paper, the aim is to conduct a benchmark on this aforementioned problem. To this end, we first collect a dataset of images with undesired and distracting elements which requires the removal of photobombing. Then, we annotate the photobombed regions which should be removed. Next, different image inpainting methods are leveraged to remove the photobombed regions and reconstruct the image. We further invited professional photoshoppers to remove the unwanted regions. These photoshopped images are considered as the groundtruth. In our benchmark, several performance metrics are leveraged to compare the results of different methods with the groundtruth. The experiments provide insightful results which demonstrate the effectiveness of inpainting methods in this particular problem.
AB - Photobombing occurs very often in photography. This causes inconvenience to the main person(s) in the photos. Therefore, there is a legitimate need to remove the photobombing from taken images to produce a pleasing image. In this paper, the aim is to conduct a benchmark on this aforementioned problem. To this end, we first collect a dataset of images with undesired and distracting elements which requires the removal of photobombing. Then, we annotate the photobombed regions which should be removed. Next, different image inpainting methods are leveraged to remove the photobombed regions and reconstruct the image. We further invited professional photoshoppers to remove the unwanted regions. These photoshopped images are considered as the groundtruth. In our benchmark, several performance metrics are leveraged to compare the results of different methods with the groundtruth. The experiments provide insightful results which demonstrate the effectiveness of inpainting methods in this particular problem.
UR - https://www.scopus.com/pages/publications/85145253344
UR - https://www.scopus.com/pages/publications/85145253344#tab=citedBy
U2 - 10.1007/978-3-031-20716-7_5
DO - 10.1007/978-3-031-20716-7_5
M3 - Conference contribution
AN - SCOPUS:85145253344
SN - 9783031207150
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 55
EP - 66
BT - Advances in Visual Computing - 17th International Symposium, ISVC 2022, Proceedings
A2 - Bebis, George
A2 - Li, Bo
A2 - Yao, Angela
A2 - Liu, Yang
A2 - Duan, Ye
A2 - Lau, Manfred
A2 - Khadka, Rajiv
A2 - Crisan, Ana
A2 - Chang, Remco
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th International Symposium on Visual Computing, ISVC 2022
Y2 - 3 October 2022 through 5 October 2022
ER -