TY - GEN
T1 - A preliminary Study on Seasonal features Understanding from Flyer Images Based on Machine Learning
AU - Tateyama, Tomoko
AU - Miyamoto, Takumi
AU - Orimoto, Ken
AU - Matsumoto, Shimpei
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Today, the keyword assignment for advertisements from within digital leaflet images is mainly done manually, and there are problems such as huge amount of task and individual differences in keyword assignment. In this study, we focus on the analysis of flyer images of seasonal events and propose an image categorization method with seasonal information. Many flyer images of seasonal events have designs that represent the season of the event, such as maple leaves in fall and cherry blossoms in spring. We hypothesized that it is possible to classify flyer images by month or season, based on the differences in the designs for each season. For this hypothesis, this study classifies the collected flyer images based on feature detection and machine learning to classify the differences in design by season, and evaluates the classification results.
AB - Today, the keyword assignment for advertisements from within digital leaflet images is mainly done manually, and there are problems such as huge amount of task and individual differences in keyword assignment. In this study, we focus on the analysis of flyer images of seasonal events and propose an image categorization method with seasonal information. Many flyer images of seasonal events have designs that represent the season of the event, such as maple leaves in fall and cherry blossoms in spring. We hypothesized that it is possible to classify flyer images by month or season, based on the differences in the designs for each season. For this hypothesis, this study classifies the collected flyer images based on feature detection and machine learning to classify the differences in design by season, and evaluates the classification results.
UR - http://www.scopus.com/inward/record.url?scp=85133179538&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85133179538&partnerID=8YFLogxK
U2 - 10.1109/IIAI-AAI53430.2021.00119
DO - 10.1109/IIAI-AAI53430.2021.00119
M3 - Conference contribution
AN - SCOPUS:85133179538
T3 - Proceedings - 2021 10th International Congress on Advanced Applied Informatics, IIAI-AAI 2021
SP - 668
EP - 673
BT - Proceedings - 2021 10th International Congress on Advanced Applied Informatics, IIAI-AAI 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Congress on Advanced Applied Informatics, IIAI-AAI 2021
Y2 - 11 July 2021 through 16 July 2021
ER -