The existing image retrieval approaches focus on the behavior of a
single user only in each query without considering the correlation of
the behaviors of multiple users in performing similar queries. In fact,
users would have similar behaviors while they have similar expectations
during queries. Accordingly, this paper therefore proposes the
interactive image retrieval framework with the Similar Behavior Learning
model. The framework consists of two stages. In the first stage, the
framework retrieves images with the content-based feature vector as
preliminary query result for user selection. In the second stage, the
SBL model determines the similarity of the user behavior and annotates
label code to the selected images instantly. The images are indexed by
label code can be retrieved more efficiently. Meanwhile, the selected
images in preliminary result are used as additional information for
retrieving better results at the end of the current query. Experiments
show the promising results.