TY - GEN
T1 - Web-based multimedia retrieval
T2 - 2nd International Conference on Web Information Systems Engineering, WISE 2001
AU - Li, Qing
AU - Yang, Jun
AU - Zhuang, Yueting
PY - 2001
Y1 - 2001
N2 - The major challenges of multimedia retrieval are the difficulty of generating semantic indexes, as well as the incapability of identifying personalized user interests. This paper attempts to address both problems by suggesting a collaborative yet personalized approach for Web-based multimedia retrieval, which employs a synergy between the relevance feedback technique from the information retrieval community, and the user profiling technique from the information filtering community. Specifically, a "common profile" is established to represent the common knowledge on the semantics of multimedia data, which allow a user to "learn from others" in the retrieval process. On the other hand, for each user a "user profile" is constructed to characterize his/her personal views, which allow a user to "learn from own history". Both types of profiles can be learned and updated incrementally from user feedback. By using an integrated retrieval algorithm based on profiles, this approach strikes the balance between exploiting the common knowledge of most users and catering for the personalized interest of a particular user. The results of some preliminary experiments have demonstrated the effectiveness of the proposed approach.
AB - The major challenges of multimedia retrieval are the difficulty of generating semantic indexes, as well as the incapability of identifying personalized user interests. This paper attempts to address both problems by suggesting a collaborative yet personalized approach for Web-based multimedia retrieval, which employs a synergy between the relevance feedback technique from the information retrieval community, and the user profiling technique from the information filtering community. Specifically, a "common profile" is established to represent the common knowledge on the semantics of multimedia data, which allow a user to "learn from others" in the retrieval process. On the other hand, for each user a "user profile" is constructed to characterize his/her personal views, which allow a user to "learn from own history". Both types of profiles can be learned and updated incrementally from user feedback. By using an integrated retrieval algorithm based on profiles, this approach strikes the balance between exploiting the common knowledge of most users and catering for the personalized interest of a particular user. The results of some preliminary experiments have demonstrated the effectiveness of the proposed approach.
KW - Collaboration
KW - Computer science
KW - Feedback
KW - Humans
KW - Image retrieval
KW - Indexing
KW - Information filtering
KW - Information retrieval
KW - Multimedia systems
KW - Search engines
UR - http://www.scopus.com/inward/record.url?scp=35048865568&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-35048865568&origin=recordpage
U2 - 10.1109/WISE.2001.996470
DO - 10.1109/WISE.2001.996470
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 076951393
SN - 9780769513935
VL - 1
SP - 92
EP - 101
BT - Proceedings of the 2nd International Conference on Web Information Systems Engineering, WISE 2001
PB - IEEE
Y2 - 3 December 2001 through 6 December 2001
ER -