Multi-query interactive image and video retrieval -: theory and practice

We propose a new interactive image and video retrieval system called multi-query interactive retrieval, which is designed to jointly optimize the retrieval performance on multiple query topics. The proposed system employs a learning-based hybrid retrieval approach, which can automatically switch between tagging and browsing interface based on user labeling efficiency. To formalize the retrieval process, we use two formal annotation models to track and estimate the retrieval time for each method. Based on the parameters of these models, the system integrates the tagging-based and browsing-based methods in order to minimize overall retrieval time across the full set of query topics. This hybrid multi-topic retrieval approach is demonstrated to be highly effective on two large-scale video collections.