To combine the complementary strengths of human vision (HV) and computer vision (CV) in target image retrieval, we proposed a brain-computer interface framework, Bayesian HV-CV Retrieval (BHCR), which couples HV with CV by a Bayesian method to retrieve target images in rapid serial visual presentation (RSVP) sequences. To construct a well-suited electroencephalogram (EEG) decoding module for BHCR, we conducted a comparative inspection on the selection of classification algorithms, and adopted linear discriminant analysis and random forests as a feature extraction method and classification algorithm, respectively. We also introduced a CV system based on convolutional neural network (CNN) as a component of BHCR. A Bayesian brain-computer interaction (BBC') module was carefully designed so that for each presented image, a Bayesian model that takes HV insight as prior information and CV insights as sample information is built up to present retrieval results. Unlike existing HV-CV coupled works that usually require extra manual labor, BHCR directly enhanced retrieval performance with the help of CV insights. As an auxiliary work and a natural extension of BHCR, we then proposed a probability propagation scheme that incorporates EEG decoding insights to improve the CV system and a one-shot image database retrieval scheme. We demonstrated the effectiveness of BHCR by extensive experiments and simulations on both the entire framework and its sub-components. The results showed the following: (1) The performance of BHCR was significantly better than the EEG-only mechanism in both receiver operating characteristic (ROC) and classification aspects; (2) The robustness of BHCR was ensured by its process flow and the steady performances of its sub-components. (C) 2017 Elsevier B.V. All rights reserved.