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[YRC17] Fully convolutional network with superpixel parsing for fashion Web image segmentation
Conférence Internationale avec comité de lecture : International Conference on Multimedia Modeling (MMM2017), January 2017, Vol. 10132(-), pp.139-151, Series LNCS, Reykjavik, Iceland, (DOI: 10.1007/978-3-319-51811-4_12)Mots clés: Fully convolutional network, deep learning, superpixel, fashion
Résumé:
In this paper we introduce a new method for extracting deformable
clothing items from still images by extending the output of a Fully Convolutional
Neural Network (FCN) to infer context from local units (superpixels). To achieve
this we optimize an energy function, which combines the large scale structure
of the image with the local low-level visual descriptions of superpixels, over the
space of all possible pixel labelings. To asses our method we compare it to the
unmodified FCN network used as a baseline, as well as to the well-known Paper
Doll and Co-parsing methods for fashion images.
Commentaires:
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BibTeX
| @inproceedings { | |||
| YRC17, | |||
| title | = | "{Fully convolutional network with superpixel parsing for fashion Web image segmentation}", | |
| author | = | " L. Yang and H. Rodrigues and M. Crucianu and M. Ferecatu ", | |
| booktitle | = | "{International Conference on Multimedia Modeling (MMM2017)}", | |
| year | = | 2017, | |
| edition | = | "-", | |
| month | = | "January", | |
| series | = | "LNCS", | |
| volume | = | 10132, | |
| issue | = | -, | |
| pages | = | "139-151", | |
| address | = | "Reykjavik, Iceland", | |
| note | = | "{-}", | |
| doi | = | "10.1007/978-3-319-51811-4_12", | |
| } | |||
