TY - GEN
T1 - Comparative Analysis of Relevance Feedback Techniques for Image Retrieval
AU - Vadicamo, Lucia
AU - Scotti, Francesca
AU - Dearle, Alan
AU - Connor, Richard
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Relevance feedback mechanisms have garnered significant attention in content-based image and video retrieval thanks to their effectiveness in refining search results to better meet user information needs. This paper provides a comprehensive comparative analysis of four techniques: Rocchio, PicHunter, Polyadic Query, and linear Support Vector Machines, representing diverse strategies encompassing query vector modification, relevance probability estimation, adaptive similarity metrics, and classifier learning. We conducted experiments within an interactive image retrieval system, with varying amounts of user feedback: full feedback, limited positive feedback, and mixed feedback. In particular, we introduce novel enhanced versions of PicHunter and Polyadic search incorporating negative feedback. Our findings highlight the benefits of integrating both positive and negative examples, demonstrating significant performance improvements. Overall, SVM and our improved PicHunter outperformed the other approaches for ad-hoc search, especially in cases in which the feedback process is iterated several times.
AB - Relevance feedback mechanisms have garnered significant attention in content-based image and video retrieval thanks to their effectiveness in refining search results to better meet user information needs. This paper provides a comprehensive comparative analysis of four techniques: Rocchio, PicHunter, Polyadic Query, and linear Support Vector Machines, representing diverse strategies encompassing query vector modification, relevance probability estimation, adaptive similarity metrics, and classifier learning. We conducted experiments within an interactive image retrieval system, with varying amounts of user feedback: full feedback, limited positive feedback, and mixed feedback. In particular, we introduce novel enhanced versions of PicHunter and Polyadic search incorporating negative feedback. Our findings highlight the benefits of integrating both positive and negative examples, demonstrating significant performance improvements. Overall, SVM and our improved PicHunter outperformed the other approaches for ad-hoc search, especially in cases in which the feedback process is iterated several times.
KW - Content-Based Image Retrieval
KW - PicHunter
KW - Polyadic Query
KW - Relevance Feedback
KW - Rocchio
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=85216124971&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-2054-8_16
DO - 10.1007/978-981-96-2054-8_16
M3 - Conference contribution
AN - SCOPUS:85216124971
SN - 9789819620531
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 206
EP - 219
BT - MultiMedia Modeling - 31st International Conference on Multimedia Modeling, MMM 2025, Proceedings
A2 - Ide, Ichiro
A2 - Kompatsiaris, Ioannis
A2 - Xu, Changsheng
A2 - Yanai, Keiji
A2 - Chu, Wei-Ta
A2 - Nitta, Naoko
A2 - Riegler, Michael
A2 - Yamasaki, Toshihiko
PB - Springer Science and Business Media
T2 - 31st International Conference on Multimedia Modeling, MMM 2025
Y2 - 8 January 2025 through 10 January 2025
ER -