Learning-Based Recommendations for Efficient Urban Visual Query

Urban visual querying leverages visual representations and interactions to depict the domain of interest and express related requests for exploring complex datasets, which is usually an iterative process. One main challenge of this process is the vast search space in terms of identifying querying conditions, observing querying results, and making the subsequent queries. This paper proposes a novel acceleration scheme that intelligently recommends a small set of querying results subject to previous queries. Central to our approach is a reinforcement learning based approach that trains a recommendation agent by simulating user behavior and characterizing the search space. We propose a mixed-initiative urban visual query scheme to enhance the exploration process additionally. We evaluate our approach by performing qualitative and quantitative experiments on a real-world scenario. The experimental results demonstrate the capability of reducing user workload, achieving optimized querying, and improving analysis efficiency.