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Ontology based classification for multi-label image annotation

Reshma, Ismat Ara; Ullah, Md Zia; Aono, Masaki


Ismat Ara Reshma

Masaki Aono


Image annotation has been an important task for visual information retrieval. It usually involves a multi-class multi-label classification problem. To solve this problem, many researches have been conducted during last two decades, although most of the proposed methods rely on the training data with the ground truth. To prepare such a ground truth is an expensive and laborious task that cannot be easily scaled, and “semantic gaps” between low-level visual features and high-level semantics still remain. In this paper, we propose a novel approach, ontology based supervised learning for multi-label image annotation, where classifiers' training is conducted using easily gathered Web data. Moreover, it takes advantage of both low-level visual features and high-level semantic information of given images. Experimental results using 0.507 million Web images database show effectiveness of the proposed framework over existing method.

Presentation Conference Type Conference Paper (Published)
Conference Name 2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)
Start Date Aug 20, 2014
End Date Aug 21, 2014
Online Publication Date Jan 12, 2015
Publication Date 2014
Deposit Date Mar 13, 2023
Publisher Institute of Electrical and Electronics Engineers
Pages 226-231
Book Title 2014 international conference of advanced informatics: concept, theory and application (ICAICTA)
Keywords noisy training data, classification, image annotation, ontology