cvpr2012_oral

Street-to-Shop: Cross-Scenario Clothing Retrieval

via Parts Alignment and Auxiliary Set-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