Contributo in atti di convegno, 2020, ENG, 10.1007/978-3-030-60936-8_27
Massoli F.V.; Falchi F.; Gennaro C.; Amato G.
CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy
Deep Learning models proved to be able to generate highly discriminative image descriptors, named deep features, suitable for similarity search tasks such as Person Re-Identification and Image Retrieval. Typically, these models are trained by employing high-resolution datasets, therefore reducing the reliability of the produced representations when low-resolution images are involved. The similarity search task becomes even more challenging in the cross-resolution scenarios, i.e., when a low-resolution query image has to be matched against a database containing descriptors generated from images at different, and usually high, resolutions. To solve this issue, we proposed a deep learning-based approach by which we empowered a ResNet-like architecture to generate resolution-robust deep features. Once trained, our models were able to generate image descriptors less brittle to resolution variations, thus being useful to fulfill a similarity search task in cross-resolution scenarios. To asses their performance, we used synthetic as well as natural low-resolution images. An immediate advantage of our approach is that there is no need for Super-Resolution techniques, thus avoiding the need to synthesize queries at higher resolutions.
Similarity Search and Applications, pp. 352–360, Copenhagen, Denmark, 20/09/2020, 2/10/2020
content-based image retrieval, face recognition, deep learning, cross-resolution
Massoli Fabio Valerio, Amato Giuseppe, Gennaro Claudio, Falchi Fabrizio
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 445013
Year: 2020
Type: Contributo in atti di convegno
Creation: 2021-02-16 09:42:59.000
Last update: 2022-04-06 08:46:57.000
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1007/978-3-030-60936-8_27
URL: https://link.springer.com/chapter/10.1007%2F978-3-030-60936-8_27
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:445013
DOI: 10.1007/978-3-030-60936-8_27
Scopus: 2-s2.0-85093852310