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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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