Multi-class Model Fitting by Energy Minimization and Mode-Seeking

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00323974" target="_blank" >RIV/68407700:21230/18:00323974 - isvavai.cz</a>

Result on the web

<a href="http://openaccess.thecvf.com/content_ECCV_2018/papers/Daniel_Barath_Multi-Class_Model_Fitting_ECCV_2018_paper.pdf" target="_blank" >http://openaccess.thecvf.com/content_ECCV_2018/papers/Daniel_Barath_Multi-Class_Model_Fitting_ECCV_2018_paper.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-01270-0_14" target="_blank" >10.1007/978-3-030-01270-0_14</a>

Result language

angličtina

Original language name

Multi-class Model Fitting by Energy Minimization and Mode-Seeking

Original language description

We propose a general formulation, called Multi-X, for multi-class multi-instance model fitting – the problem of interpreting the input data as a mixture of noisy observations originating from multiple instances of multiple classes. We extend the commonly used α-expansion-based technique with a new move in the label space. The move replaces a set of labels with the corresponding density mode in the model parameter domain, thus achieving fast and robust optimization. Key optimization parameters like the bandwidth of the mode seeking are set automatically within the algorithm. Considering that a group of outliers may form spatially coherent structures in the data, we propose a cross-validation-based technique removing statistically insignificant instances. Multi-X outperforms significantly the state-of-the-art on publicly available datasets for diverse problems: multiple plane and rigid motion detection; motion segmentation; simultaneous plane and cylinder fitting; circle and line fitting.

Czech name

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Czech description

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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)

Project

<a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

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

Article name in the collection

ECCV2018: Proceedings of the European Conference on Computer Vision, Part XVI

ISBN

978-3-030-01269-4

ISSN

0302-9743

e-ISSN

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Number of pages

17

Pages from-to

229-245

Publisher name

Springer, Cham

Place of publication

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Event location

Munich

Event date

Sep 8, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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