Affordable Access

deepdyve-link
Publisher Website

Supervised Deep Feature Embedding With Handcrafted Feature.

Authors
  • Kan, Shichao
  • Cen, Yigang
  • He, Zhihai
  • Zhang, Zhi
  • Zhang, Linna
  • Wang, Yanhong
Type
Published Article
Journal
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Dec 01, 2019
Volume
28
Issue
12
Pages
5809–5823
Identifiers
DOI: 10.1109/TIP.2019.2901407
PMID: 30802863
Source
Medline
Language
English
License
Unknown

Abstract

Image representation methods based on deep convolutional neural networks (CNNs) have achieved the state-of-the-art performance in various computer vision tasks, such as image retrieval and person re-identification. We recognize that more discriminative feature embeddings can be learned with supervised deep metric learning and handcrafted features for image retrieval and similar applications. In this paper, we propose a new supervised deep feature embedding with a handcrafted feature model. To fuse handcrafted feature information into CNNs and realize feature embeddings, a general fusion unit is proposed (called Fusion-Net). We also define a network loss function with image label information to realize supervised deep metric learning. Our extensive experimental results on the Stanford online products' data set and the in-shop clothes retrieval data set demonstrate that our proposed methods outperform the existing state-of-the-art methods of image retrieval by a large margin. Moreover, we also explore the applications of the proposed methods in person re-identification and vehicle re-identification; the experimental results demonstrate both the effectiveness and efficiency of the proposed methods.

Report this publication

Statistics

Seen <100 times