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2016 | OriginalPaper | Buchkapitel

Fashion Landmark Detection in the Wild

verfasst von : Ziwei Liu, Sijie Yan, Ping Luo, Xiaogang Wang, Xiaoou Tang

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

Visual fashion analysis has attracted many attentions in the recent years. Previous work represented clothing regions by either bounding boxes or human joints. This work presents fashion landmark detection or fashion alignment, which is to predict the positions of functional key points defined on the fashion items, such as the corners of neckline, hemline, and cuff. To encourage future studies, we introduce a fashion landmark dataset (The dataset is available at http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html.) with over 120K images, where each image is labeled with eight landmarks. With this dataset, we study fashion alignment by cascading multiple convolutional neural networks in three stages. These stages gradually improve the accuracies of landmark predictions. Extensive experiments demonstrate the effectiveness of the proposed method, as well as its generalization ability to pose estimation. Fashion landmark is also compared to clothing bounding boxes and human joints in two applications, fashion attribute prediction and clothes retrieval, showing that fashion landmark is a more discriminative representation to understand fashion images.

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Vorheriges Kapitel Fundamental Matrices from Moving Objects Using Line Motion Barcodes

Nächstes Kapitel Human Pose Estimation Using Deep Consensus Voting

Three states of visibility are defined for each landmark, including visible (located inside of the image and visible), invisible (inside of the image but occluded), and truncated/cut-off (outside of the image).

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Titel: Fashion Landmark Detection in the Wild
verfasst von: Ziwei Liu
Sijie Yan
Ping Luo
Xiaogang Wang
Xiaoou Tang
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2016
Print ISBN: 978-3-319-46474-9

Electronic ISBN: 978-3-319-46475-6

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-46475-6_15

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