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Mobile Visual Clothing Search

Mobile Visual Clothing Search
Mobile Visual Clothing Search
We present a mobile visual clothing search system whereby a smart phone user can either choose a social networking photo or take a new photo of a person wearing clothing of interest and search for similar clothing in a retail database. From the query image, the person is detected, clothing is segmented, and clothing features are extracted and quantized. The information is sent from the phone client to a server, where the feature vector of the query image is used to retrieve similar clothing products from online databases. The phone's GPS location is used to re-rank results by retail store location. State of the art work focusses primarily on the recognition of a diverse range of clothing offline and pays little attention to practical applications. Evaluated on a challenging dataset, the system is relatively fast and achieves promising results.
Cushen, George
52f73d41-3ae0-4c11-a50a-86e782c03745
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Cushen, George
52f73d41-3ae0-4c11-a50a-86e782c03745
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Cushen, George and Nixon, Mark S. (2013) Mobile Visual Clothing Search. IEEE International Workshop on Intelligent Mobile Vision. (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

We present a mobile visual clothing search system whereby a smart phone user can either choose a social networking photo or take a new photo of a person wearing clothing of interest and search for similar clothing in a retail database. From the query image, the person is detected, clothing is segmented, and clothing features are extracted and quantized. The information is sent from the phone client to a server, where the feature vector of the query image is used to retrieve similar clothing products from online databases. The phone's GPS location is used to re-rank results by retail store location. State of the art work focusses primarily on the recognition of a diverse range of clothing offline and pays little attention to practical applications. Evaluated on a challenging dataset, the system is relatively fast and achieves promising results.

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More information

Accepted/In Press date: April 2013
Venue - Dates: IEEE International Workshop on Intelligent Mobile Vision, 2013-03-31
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 352095
URI: http://eprints.soton.ac.uk/id/eprint/352095
PURE UUID: 03aae393-d43d-48ae-a45b-893ed713c8f5
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 01 May 2013 13:37
Last modified: 08 Jul 2020 00:23

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Contributors

Author: George Cushen
Author: Mark S. Nixon ORCID iD

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