Description: In this paper, we address a practical problem of crossscenario
clothing retrieval- given a daily human photo captured
in general environment, e.g., on street, finding similar
clothing in online shops, where the photos are captured
more professionally and with clean background. There are
large discrepancies between daily photo scenario and online
shopping scenario.
We first propose to alleviate the human pose discrepancy
by locating 30 human parts detected by a well trained human
detector. Then, founded on part features, we propose
a two-step calculation to obtain more reliable one-to-many
similarities between the query daily photo and online shopping
photos: 1) the within-scenario one-to-many similarities
between a query daily photo and the auxiliary set are
derived by direct sparse reconstruction and 2) by a crossscenario
many-to-many similarity transfer matrix inferred
offline from an extra auxiliary set and the online shopping
set, the reliable cross-scenario one-t
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