NDLI: Wise optimisation: deep image embedding by informative pair weighting and ranked list learning

Content Provider	IET Digital Library
Author	Fan, Lili Zhao, Hongwei Zhao, Haoyu
Abstract	Deep image embedding learns how to map images onto feature vectors. Image retrieval performance is often used to evaluate embedding quality. In this study, the authors proposed a wise deep image embedding optimisation (WDIEO) algorithm based on informative pair weighting and ranked list learning (IPWRLL) for network optimisation of fine-grained image retrieval. First, a hard sample mining method Top-k is proposed to select positive and negative samples. Then, for the selected query sample, a ranking list is obtained by comparing the similarity between samples in the data set and the query sample, and the sample is labelled according to the similarity. Finally, for positive samples, two optimisation rules with different functions are used, while ensuring two key issues of instance weighting and intra-class data distribution. For negative samples, different from the widely adopted methods based on the weight of sample information, the authors’ algorithm's weights are set according to the ranking list, which keeps the inter-class data distribution and the optimisation direction consistent with the loss reduction direction. The WDIEO-IPWRLL model is an end-to-end optimisation that can share parameters in the testing process. Experiments show that their proposed model achieves the state-of-the-art performance on the benchmark data set.
Starting Page	3802
Ending Page	3811
Page Count	10
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 15, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.0454
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/15
Journal	IET Image Processing
Publisher Date	2020-12-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Data Handling Technique Data Mining Embedding Quality End-to-end Optimisation Feature Extraction Fine-grained Image Retrieval Hard Sample Mining Method Top-k Image Representation Image Retrieval Image Retrieval Performance Information Retrieval Technique Informative Pair Weighting Instance Weighting Learning in AI Map Image Negative Sample Network Optimisation Optical, Image And Video Signal Processing Optimisation Optimisation Direction Optimisation Rules Optimisation Technique Positive Sample Ranked List Learning Ranking List Sample Information Selected Query Sample Wise Deep Image Embedding Optimisation Algorithm Wise Optimisation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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