【文件属性】：
文件名称：cvpr2012_oral_clothes
文件大小：1.05MB
文件格式：ZIP
更新时间：2015-12-27 13:42:33
Image Retrieval
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-to-many similarities between
the query daily photo and all online shopping photos
are obtained.
We collect a large online shopping dataset and a daily
photo dataset, both of which are thoroughly labeled with 15
clothing attributes via Mechanic Turk. The extensive experimental
evaluations on the collected datasets well demonstrate
the effectiveness of the proposed framework for crossscenario
clothing retrieval.
【文件预览】：
clothes-CVPR12.pdf