NDLI: An improved deep learning architecture for person re-identification

Content Provider	IEEE Xplore Digital Library
Author	Ahmed, E. Jones, M. Marks, T.K.
Copyright Year	2015
Description	Author affiliation: Univ. of Maryland, College Park, MD, USA (Ahmed, E.) \|\| Mitsubishi Electr. Res. Labs., Cambridge, MA, USA (Jones, M.; Marks, T.K.)
Abstract	In this work, we propose a method for simultaneously learning features and a corresponding similarity metric for person re-identification. We present a deep convolutional architecture with layers specially designed to address the problem of re-identification. Given a pair of images as input, our network outputs a similarity value indicating whether the two input images depict the same person. Novel elements of our architecture include a layer that computes cross-input neighborhood differences, which capture local relationships between the two input images based on mid-level features from each input image. A high-level summary of the outputs of this layer is computed by a layer of patch summary features, which are then spatially integrated in subsequent layers. Our method significantly outperforms the state of the art on both a large data set (CUHK03) and a medium-sized data set (CUHK01), and is resistant to over-fitting. We also demonstrate that by initially training on an unrelated large data set before fine-tuning on a small target data set, our network can achieve results comparable to the state of the art even on a small data set (VIPeR).
Starting Page	3908
Ending Page	3916
File Size	570590
Page Count	9
File Format	PDF
ISSN	10636919
e-ISBN	9781467369640
DOI	10.1109/CVPR.2015.7299016
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer architecture Convolution Measurement Machine learning Training Feature extraction Image color analysis
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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