All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Robustness of Supervised Learning Based on Combined Centroids

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00547523" target="_blank" >RIV/67985807:_____/21:00547523 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1007/978-3-030-87897-9_16" target="_blank" >http://dx.doi.org/10.1007/978-3-030-87897-9_16</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-030-87897-9_16" target="_blank" >10.1007/978-3-030-87897-9_16</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Robustness of Supervised Learning Based on Combined Centroids

  • Original language description

    Recently, we proposed a novel sparse centroid-based supervised learning method, allowing to optimize a single centroid and its corresponding weights. The method is especially useful for localizing objects in images. Here, we extend the method to the task of joint localization of several objects in a 2D-image by means of combining several centroids. The novel approach, i.e. joint optimization of several centroids and a subsequent optimization of their weights, is illustrated on the task of localizing the mouth and both eyes in facial images. Because we are particularly interested in studying the robustness of the method to various modifications of the images, we evaluate the performance of the methods also over images artificially modified by additional noise, occlusion, changed illumination, or rotation. The novel centroid-based method is successful in the localization task, and the optimization turns out to ensure robustness with respect to the presence of noise or occlusion in the images. Moreover, combining the optimized centroids yields more robust results than a method using simple centroids with a highly robust correlation coefficient (with a high breakdown point).

  • 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

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Artificial Intelligence and Soft Computing. ICAISC 2021 Proceedings, Part II

  • ISBN

    978-3-030-87896-2

  • ISSN

    0302-9743

  • e-ISSN

  • Number of pages

    12

  • Pages from-to

    171-182

  • Publisher name

    Springer

  • Place of publication

    Cham

  • Event location

    Zakopane / Virtual

  • Event date

    Jun 20, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article