hugo-academic/exampleSite/content/publication/clothing-search/index.md

+++ title = "Mobile visual clothing search" date = 2013-07-01T00:00:00

Authors. Comma separated list, e.g. ["Bob Smith", "David Jones"].

authors = ["GA Cushen", "MS Nixon"]

Publication type.

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0 = Uncategorized

1 = Conference paper

2 = Journal article

3 = Manuscript

4 = Report

5 = Book

6 = Book section

publication_types = ["1"]

Publication name and optional abbreviated version.

publication = "In International Conference on Multimedia and Expo Workshops (ICMEW), IEEE." publication_short = "In ICMEW"

Abstract.

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 focuses 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."

Summary. An optional shortened abstract.

summary = "A mobile visual clothing search system is presented whereby a smart phone user can either choose a social networking image or capture a new photo of a person wearing clothing of interest and search for similar clothing in a large cloud-based ecommerce database. The phone's GPS location is used to re-rank results by retail store location, to inform the user of local stores where similar clothing items can be tried on."

Digital Object Identifier (DOI)

doi = ""

Is this a featured publication? (true/false)

featured = true

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tags = []

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content/project/deep-learning/index.md.

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projects = ["internal-project"]

Links (optional).

url_pdf = "http://eprints.soton.ac.uk/352095/1/Cushen-IMV2013.pdf" url_preprint = "http://eprints.soton.ac.uk/352095/1/Cushen-IMV2013.pdf" url_code = "#" url_dataset = "#" url_project = "" url_slides = "#" url_video = "#" url_poster = "#" url_source = "#"

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math = true

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More detail can easily be written here using Markdown and $\rm \LaTeX$ math code.