Evaluation of Local Descriptors for Automatic Image Annotation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43931925" target="_blank" >RIV/49777513:23520/17:43931925 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5220/0006194305270534" target="_blank" >http://dx.doi.org/10.5220/0006194305270534</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0006194305270534" target="_blank" >10.5220/0006194305270534</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation of Local Descriptors for Automatic Image Annotation

Popis výsledku v původním jazyce

In this paper we aim at evaluation of three local descriptors for the automatic image annotation (AIA) task. LBP, POEM and LDP descriptors are successfully used in many other domains such as face recognition. However, the utilization of them in the AIA field is rather infrequent. The annotation algorithm is based on the K-nearest neighbours (KNN) classifier where labels from K most similar images are “transferred” to the annotated one. We propose a label transfer method that assigns variable number of labels to each image. It is compared with an existing approach using constant number of labels. The proposed method is evaluated on three image datasets: Li photography, IAPR-TC12 and ESP. We show that the results of the utilized local descriptors are comparable to, and in many cases outperform the texture features usually used in AIA. We also show that the proposed label transfer method can increase the overall system performance especially for the IAPR-TC12 dataset.

Název v anglickém jazyce

Evaluation of Local Descriptors for Automatic Image Annotation

Popis výsledku anglicky

In this paper we aim at evaluation of three local descriptors for the automatic image annotation (AIA) task. LBP, POEM and LDP descriptors are successfully used in many other domains such as face recognition. However, the utilization of them in the AIA field is rather infrequent. The annotation algorithm is based on the K-nearest neighbours (KNN) classifier where labels from K most similar images are “transferred” to the annotated one. We propose a label transfer method that assigns variable number of labels to each image. It is compared with an existing approach using constant number of labels. The proposed method is evaluated on three image datasets: Li photography, IAPR-TC12 and ESP. We show that the results of the utilized local descriptors are comparable to, and in many cases outperform the texture features usually used in AIA. We also show that the proposed label transfer method can increase the overall system performance especially for the IAPR-TC12 dataset.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LO1506" target="_blank" >LO1506: Podpora udržitelnosti centra NTIS - Nové technologie pro informační společnost</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 9th International Conference on Agents and Artificial Intelligence (ICAART 2017)

ISBN

978-989-758-220-2

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

8

Strana od-do

527-534

Název nakladatele

SciTePress

Místo vydání

Setúbal

Místo konání akce

Porto

Datum konání akce

24. 2. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000413244200055