Edge Augmentation for Large-Scale Sketch Recognition without Sketches
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00362153" target="_blank" >RIV/68407700:21230/22:00362153 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICPR56361.2022.9956233" target="_blank" >https://doi.org/10.1109/ICPR56361.2022.9956233</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICPR56361.2022.9956233" target="_blank" >10.1109/ICPR56361.2022.9956233</a>
Alternative languages
Result language
angličtina
Original language name
Edge Augmentation for Large-Scale Sketch Recognition without Sketches
Original language description
This work addresses scaling up the sketch classification task into a large number of categories. Collecting sketches for training is a slow and tedious process that has so far precluded any attempts to large-scale sketch recognition. We overcome the lack of training sketch data by exploiting labeled collections of natural images that are easier to obtain. To bridge the domain gap we present a novel augmentation technique that is tailored to the task of learning sketch recognition from a training set of natural images. Randomization is introduced in the parameters of edge detection and edge selection. Natural images are translated to a pseudo-novel domain called "randomized Binary Thin Edges" (rBTE), which is used as a training domain instead of natural images. The ability to scale up is demonstrated by training CNN-based sketch recognition of more than 2.5 times larger number of categories than used previously. For this purpose, a dataset of natural images from 874 categories is constructed by combining a number of popular computer vision datasets. The categories are selected to be suitable for sketch recognition. To estimate the performance, a subset of 393 categories with sketches is also collected.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2022 26th International Conference on Pattern Recognition (ICPR)
ISBN
978-1-6654-9062-7
ISSN
1051-4651
e-ISSN
2831-7475
Number of pages
8
Pages from-to
3595-3602
Publisher name
IEEE
Place of publication
Piscataway
Event location
Montreal
Event date
Aug 21, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000897707603084