TY - GEN
T1 - Is Whole Object Information Helpful for Scene Recognition?
AU - Seong, Hongje
AU - Hyun, Junhyuk
AU - Kim, Euntai
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Scene recognition is one of the visual tasks, classifying a place category on an image. Scene images may contain various objects, and these objects tend to become clues to recognize the scene of the image. Therefore, many previous approaches for scene recognition use the object information that appeared in the image to improve the performance. Here, we raise a question of whether whole object information is helpful for scene recognition. To find the answer to the question, we conduct experiments on Places365, which is the largest scene recognition dataset consist of real-world images. To find the object classes which disturbed scene recognition, we utilize the Class Conversion Matrix, which is a deep learning approach. Finally, we found that some object classes may contribute to disturbing scene recognition. It indicates that not only making good use of object information, but also dropping disturbed object information is also important.
AB - Scene recognition is one of the visual tasks, classifying a place category on an image. Scene images may contain various objects, and these objects tend to become clues to recognize the scene of the image. Therefore, many previous approaches for scene recognition use the object information that appeared in the image to improve the performance. Here, we raise a question of whether whole object information is helpful for scene recognition. To find the answer to the question, we conduct experiments on Places365, which is the largest scene recognition dataset consist of real-world images. To find the object classes which disturbed scene recognition, we utilize the Class Conversion Matrix, which is a deep learning approach. Finally, we found that some object classes may contribute to disturbing scene recognition. It indicates that not only making good use of object information, but also dropping disturbed object information is also important.
UR - http://www.scopus.com/inward/record.url?scp=85094321890&partnerID=8YFLogxK
U2 - 10.1109/UR49135.2020.9144930
DO - 10.1109/UR49135.2020.9144930
M3 - Conference contribution
AN - SCOPUS:85094321890
T3 - 2020 17th International Conference on Ubiquitous Robots, UR 2020
SP - 149
EP - 152
BT - 2020 17th International Conference on Ubiquitous Robots, UR 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Conference on Ubiquitous Robots, UR 2020
Y2 - 22 June 2020 through 26 June 2020
ER -