A Clothing Retrieval Model Based on DenseNet and Multi-Similarity Loss. (November 2020)
- Record Type:
- Journal Article
- Title:
- A Clothing Retrieval Model Based on DenseNet and Multi-Similarity Loss. (November 2020)
- Main Title:
- A Clothing Retrieval Model Based on DenseNet and Multi-Similarity Loss
- Authors:
- Feifei, Xu
Yu, Tian
Shuting, Zheng - Abstract:
- Abstract: With the rapid development of deep learning in recent years, Deep Neural Network structure has been applied to all aspects of image algorithm, including image classification and recognition, target detection, image retrieval. Image retrieval is suffering from some challenges, such as crease, cover, background. Based on these challenges, using the local information of the image to replace the image is proposed which learns embedding vectors by local information to reduce noise interference. Inspired by the above discussion, a new deep learning model named IAPM (Integrated and Partial Model) is proposed in this paper. According to global information and local information, Joint training is carried out. For feature extraction, DenseNet is used to extract the feature vector. And the idea of transfer learning is applied to transform classification problems to retrieval problems. Meanwhile, the multi-task model is adopted to construct a framework, and Multi-Similarity Loss is used to pull the position of the embedded vector in feature space. Experimental results show its effectiveness.
- Is Part Of:
- Journal of physics. Volume 1684(2020)
- Journal:
- Journal of physics
- Issue:
- Volume 1684(2020)
- Issue Display:
- Volume 1684, Issue 1 (2020)
- Year:
- 2020
- Volume:
- 1684
- Issue:
- 1
- Issue Sort Value:
- 2020-1684-0001-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-11
- Subjects:
- Physics -- Congresses
530.5 - Journal URLs:
- http://www.iop.org/EJ/journal/1742-6596 ↗
http://ioppublishing.org/ ↗ - DOI:
- 10.1088/1742-6596/1684/1/012054 ↗
- Languages:
- English
- ISSNs:
- 1742-6588
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5036.223000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 25478.xml