TY - GEN
T1 - XQM: Interactive Learning on Mobile Phones
AU - Bagi, Alexandra M.
AU - Schild, Kim I.
AU - Khan, Omar Shahbaz
AU - Zahálka, Jan
AU - Jónsson, Björn Thór
PY - 2021/6
Y1 - 2021/6
N2 - There is an increasing need for intelligent interaction with media collections, and mobile phones are gaining significant traction as the device of choice for many users. In this paper, we present XQM, a mobile approach for intelligent interaction with the user’s media on the phone, tackling the inherent challenges of the highly dynamic nature of mobile media collections and limited computational resources of the mobile device. We employ interactive learning, a method that conducts interaction rounds with the user, each consisting of the system suggesting relevant images based on its current model, the user providing relevance labels, the system’s model retraining itself based on these labels, and the system obtaining a new set of suggestions for the next round. This method is suitable for the dynamic nature of mobile media collections and the limited computational resources. We show that XQM, a full-fledged app implemented for Android, operates on 10K image collections in interactive time (less than 1.4 s per interaction round), and evaluate user experience in a user study that confirms XQM’s effectiveness.
AB - There is an increasing need for intelligent interaction with media collections, and mobile phones are gaining significant traction as the device of choice for many users. In this paper, we present XQM, a mobile approach for intelligent interaction with the user’s media on the phone, tackling the inherent challenges of the highly dynamic nature of mobile media collections and limited computational resources of the mobile device. We employ interactive learning, a method that conducts interaction rounds with the user, each consisting of the system suggesting relevant images based on its current model, the user providing relevance labels, the system’s model retraining itself based on these labels, and the system obtaining a new set of suggestions for the next round. This method is suitable for the dynamic nature of mobile media collections and the limited computational resources. We show that XQM, a full-fledged app implemented for Android, operates on 10K image collections in interactive time (less than 1.4 s per interaction round), and evaluate user experience in a user study that confirms XQM’s effectiveness.
KW - Interactive learning
KW - Relevance feedback
KW - Mobile devices
U2 - 10.1007%2F978-3-030-67835-7_24
DO - 10.1007%2F978-3-030-67835-7_24
M3 - Article in proceedings
SN - 978-3-030-67834-0
T3 - Lecture Notes in Computer Science
SP - 281
EP - 293
BT - MultiMedia Modeling - 27th International Conference, MMM 2021, Prague, Czech Republic, June 22-24, 2021, Proceedings, Part II.
PB - Springer
CY - Prague, Czech Republic (virtual)
ER -