New-Fangled Alignment of Ontologies for Content Based Semantic Image Retrieval

Khodaskar, Anuja; Ladhake, S. A.

doi:10.1016/j.procs.2015.04.185

Cited by 8 publications

(2 citation statements)

References 9 publications

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“…However, when such ontology is integrated with too much enriched vocabulary, especially the DBPedia which contains 2.6 million entities and 4.7 billion pieces of information, the performance of the precision rate would be low when many unrelated images results returned from the queries. Khodaskar & Ladhake, (2015) embarked on the image retrieval system using an alignment of ontologies in order to reduce the semantic gap and provide highly accurate, efficient and effective image retrieval results. They proposed multiple ontologies which merged feature ontology, semantic feature ontology, user ontology and metadata ontology to overcome the traditional image retrieval system that used the single ontology which retrieved imprecise images.…”

Section: Ontology and Image Retrieval Systemmentioning

confidence: 99%

An Object Properties Filter for Multi-Modality Ontology Semantic Image Retrieval

Sulaiman

Nordin

Jamil

2016

JICT

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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.

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Section: Ontology and Image Retrieval Systemmentioning

confidence: 99%

An Object Properties Filter for Multi-Modality Ontology Semantic Image Retrieval

Sulaiman

Nordin

Jamil

2016

JICT

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“…Image retrieval searches relevant images in huge medical records using characteristic features associated with images. To help radiologist in searching required image from large datasets effective Content Based Medical Image Retrieval (CBMIR) development is necessary [1]. To manage large medical database images, numerous automatic retrieval based algorithms are proposed.…”

Section: Introductionmentioning

confidence: 99%

Content Based Retrieval of Liver Images for Computer Aided Diagnosis

H L*,

M V

2019

IJEAT

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Liver cancer is a serious disease caused by a variety of factors that damage the liver region. Early detection of this disease is necessary to diagnose and to cure it completely. Enormous increase in medical database images has lead to development of Content Based Image Retrieval (CBIR) system to retrieve relevant liver images from medical database consisting of abdominal Computed Tomography (CT) images. In the proposed method Content Based Medical Image Retrieval (CBMIR) system is designed to search and retrieve relevant liver images from medical image database. Adaptive Region Growing Algorithm (ARGA) and Simple Linear Iterative Clustering (SLIC) are used for liver and tumor segmentation. Features are extracted using Gray Level Co-occurrence Matrix (GLCM), Average Correction High order Local Autocorrelation Coefficients (ACHLAC) and Legendre Moments (LM). Based on the distance metric, distances between extracted features of query image and images in the database are measured. Euclidean distance metric is used to retrieve relevant medical images.

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Region Based Semantic Image Retrieval Using Ontology

Kolla

Gopal²

2017

Lecture Notes in Networks and Systems

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New-Fangled Alignment of Ontologies for Content Based Semantic Image Retrieval

Cited by 8 publications

References 9 publications

An Object Properties Filter for Multi-Modality Ontology Semantic Image Retrieval

An Object Properties Filter for Multi-Modality Ontology Semantic Image Retrieval

Content Based Retrieval of Liver Images for Computer Aided Diagnosis

Region Based Semantic Image Retrieval Using Ontology

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