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Class-Based Variational Representation Learning For Robust Image Retrieval

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing (ICIP)
PublisherIEEE
Pages854-858
Number of pages5
ISBN (Electronic)978-1-5386-6249-6
ISBN (Print)978-1-5386-6250-2
DOIs
Publication statusPublished - Sep 2019
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Image Processing -
Duration: 1 Jan 1900 → …

Publication series

NameIEEE International Conference on Image Processing
ISSN (Print)1522-4880
ISSN (Electronic)2381-8549

Conference

ConferenceIEEE International Conference on Image Processing
Period1/01/00 → …

Abstract

Supervised learning for Content-based Information Retrieval allows for obtaining discriminative representations that often excel within the training domain. However, recent evidence suggests that these representations can actually harm the retrieval precision for queries that do not belong to the domain of the training set compared to other, less discriminative representations. To avoid this behavior, we propose to learn discriminative representations which also encode the latent generative factors for each class. In this way, the proposed method is capable of maintaining (part of) the in-class variance, as well as being able to represent data that belong to classes that were not seen during the training by better learning the structure of the input space. The proposed method is evaluated under different in-domain and out-of-domain setups, significantly outperforming existing supervised and unsupervised representation learning approaches.

Keywords

  • Training, Task analysis, Feature extraction, Image retrieval, Image reconstruction, Optimization, Data mining, Image Retrieval, Supervised Representation Learning, Metric Learning

Publication forum classification

Field of science, Statistics Finland