| Concept Learning and
Transplantation for Dynamic Image Databases
Presented by: Anlei Dong
ABSTRACT: The task of a content-based image retrieval (CBIR)
system is to cater to users who expect to get relevant images
with high precision and efficiency in response to query images.
This paper presents a concept learning approach based on a
new user directed semi-supervised EM algorithm (SS-EM).
The system integrates a mixture model of the data, relevance
feedback and long-term continuous learning. The concepts are
incrementally refined with increased retrieval experiences.
The concept knowledge can be immediately transplanted to deal
with the dynamic database situations such as insertion of
new images, removal of existing images and query images which
are outside the database. Experiment results on Corel database
show the efficacy and the improvement in retrieval performance
of our proposed concept learning and transplantation approaches.