Next Article in Journal
Classification of Areas Suitable for Fish Farming Using Geotechnology and Multi-Criteria Analysis
Next Article in Special Issue
Multi-Scale Remote Sensing Semantic Analysis Based on a Global Perspective
Previous Article in Journal
An On-Demand Scalable Model for Geographic Information System (GIS) Data Processing in a Cloud GIS
Previous Article in Special Issue
Using Vehicle Synthesis Generative Adversarial Networks to Improve Vehicle Detection in Remote Sensing Images
Open AccessArticle

Image Retrieval Based on Learning to Rank and Multiple Loss

1
College of Computer Science and Technology, Jilin University, Changchun 130012, China
2
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
3
State Key Laboratory of Applied Optics, Changchun 130033, China
4
Editorial Department of Journal (Engineering and Technology Edition), Jilin University, Changchun 130012, China
5
School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(9), 393; https://doi.org/10.3390/ijgi8090393
Received: 26 June 2019 / Revised: 14 August 2019 / Accepted: 26 August 2019 / Published: 4 September 2019
(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)
Image retrieval applying deep convolutional features has achieved the most advanced performance in most standard benchmark tests. In image retrieval, deep metric learning (DML) plays a key role and aims to capture semantic similarity information carried by data points. However, two factors may impede the accuracy of image retrieval. First, when learning the similarity of negative examples, current methods separate negative pairs into equal distance in the embedding space. Thus, the intraclass data distribution might be missed. Second, given a query, either a fraction of data points, or all of them, are incorporated to build up the similarity structure, which makes it rather complex to calculate similarity or to choose example pairs. In this study, in order to achieve more accurate image retrieval, we proposed a method based on learning to rank and multiple loss (LRML). To address the first problem, through learning the ranking sequence, we separate the negative pairs from the query image into different distance. To tackle the second problem, we used a positive example in the gallery and negative sets from the bottom five ranked by similarity, thereby enhancing training efficiency. Our significant experimental results demonstrate that the proposed method achieves state-of-the-art performance on three widely used benchmarks. View Full-Text
Keywords: multiple loss function; computer vision; deep image retrieval; learning to rank; deep learning multiple loss function; computer vision; deep image retrieval; learning to rank; deep learning
Show Figures

Figure 1

MDPI and ACS Style

Fan, L.; Zhao, H.; Zhao, H.; Liu, P.; Hu, H. Image Retrieval Based on Learning to Rank and Multiple Loss. ISPRS Int. J. Geo-Inf. 2019, 8, 393.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop