Multiple User Behavior Learning for Enhancing Interactive Image Retrieval
preprintposted on 30.05.2020, 16:55 by Yiu-ming Cheung, Sheung Wai Chan
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.