  Image-based clothing retrieval is receiving increasing interest with the
growth of online shopping. In practice, users may often have a desired piece of
clothing in mind (e.g., either having seen it before on the street or requiring
certain specific clothing attributes) but may be unable to supply an image as a
query. We model this problem as a new type of image retrieval task in which the
target image resides only in the user's mind (called "mental image retrieval"
hereafter). Because of the absence of an explicit query image, we propose to
solve this problem through relevance feedback. Specifically, a new Bayesian
formulation is proposed that simultaneously models the retrieval target and its
high-level representation in the mind of the user (called the "user metric"
hereafter) as posterior distributions of pre-fetched shop images and
heterogeneous features extracted from multiple clothing attributes,
respectively. Requiring only clicks as user feedback, the proposed algorithm is
able to account for the variability in human decision-making. Experiments with
real users demonstrate the effectiveness of the proposed algorithm.

Computer Vision and Pattern Recognition;Information Retrieval