ABSTRACT:
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.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
Zhuoxiang Chen, Zhe Xu, Ya Zhang, Member, IEEE, and Xiao Gu,
“Query-free Clothing Retrieval via Implicit Relevance Feedback”, IEEE
Transactions on Multimedia, 2018.