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OntoAnnClass: ontology-based image annotation driven by classification using HMAX features
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DOI：10.1007/s11042-020-09864-9.png)
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Abstract
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Several approaches have been proposed in the area of Automatic Image Annotation (AIA) in order to exploit the relationships between words that are extracted from image categories, and to automatically generate annotation words for a given image. Other methods exploit ontologies, where the annotation keywords were derived from ontology to improve image annotation. In this paper, we propose an ontology-based image annotation driven by classification using HMAX features. The idea is (1) to train visual-feature-classifiers and to build an ontology that can finely represent the semantic information associated with training images, and (2) to combine classifier outputs and ontology for image annotation. To annotate images, we define a membership value of words in images. In particular, we propose to evaluate the membership value based on the confidence value of classifiers and the semantic similarity between words. The membership value depends on the word relationships found in the ontology that serve to select annotation words. The obtained experimental results show that the exploitation of both classifier outputs and ontology by evaluating our proposed membership value enables an improvement of image annotation.
Keywords:
Image annotation
Ontologies
Classification
HMAX features
BoVW model
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