Abstract
Ontology is a semantic technology that provides the possible approach to bridge the issue on semantic gap in image retrieval between low-level visual features and high-level human semantic. The semantic gap occurs when there is a discrepancy between the information that is extracted from visual data and the text description. In other words, there is a difference between the computational representation in machine and human natural language. In this paper, an ontology has been utilized to reduce the semantic gap by developing a multi-modality ontology image retrieval with the enhancement of a retrieval mechanism by using the object properties filter. To achieve this, a multi-modality ontology semantic image framework was proposed, comprising of four main components which were resource identification, information extraction, knowledge-based construction and retrieval mechanism. A new approach, namely object properties filter is proposed by customizing the semantic image retrieval algorithm and the graphical user interface to facilitate the user to engage with the machine i.e. computers, in order to enhance the retrieval performance. The experiment results showed that the proposed approach delivered better results compared to the approach that did not use the object properties filter based on probability precision measurement.
Highlights
With the current advancement of technology, the image retrieval (IMR) have become important in research over the last four decades as there was a need to control and manage the collection of large images effectively (Rui, Huang, & Chang, 1999)
The initial stage of the IMR method was called the text-based image retrieval (TBIR), which used text associated with a certain image to determine what the image contained (Riad, Elminir, & Abd-Elghany, 2012)
This study explored the significance and the impacts of object properties filter which can be exploited in the retrieval mechanism in order to achieve the main goals in IMR, to increase the relevance and accuracy of digital images retrieval
Summary
With the current advancement of technology, the image retrieval (IMR) have become important in research over the last four decades as there was a need to control and manage the collection of large images effectively (Rui, Huang, & Chang, 1999). There were many algorithms that have been developed to describe the low-level features (Zhang, Islam, & Lu, 2012) These algorithms failed to model the image semantics as how humans interpret the images (Zhang, 2007). The semantic-based image retrieval (SBIR), had been proposed as a possible solution to bridge the semantic gap (Smeulders, Worring, Santini, Gupta, & Jain, 2000) between low-level features and highlevel human semantic. The World Health Organization (WHO) (WHO, 2008) stated that between 70% to 80% of developed countries used alternative medicines for health purposes This high percentage portrays the crucial need of semantic descriptions for the herbal medicinal plant images to cater for various users’ information needs. This study explored the significance and the impacts of object properties filter which can be exploited in the retrieval mechanism in order to achieve the main goals in IMR, to increase the relevance and accuracy of digital images retrieval
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