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SIR-DL: AN ARCHITECTURE OF SEMANTIC-BASED IMAGE RETRIEVAL USING DEEP LEARNING TECHNIQUE AND RDF TRIPLE LANGUAGE

Van The Thanh, Do Quang Khoi, Le Huu Ha, Le Manh Thanh

Abstract


The problem of finding and identifying semantics of images is applied in multimedia applications of many different fields such as Hospital Information System, Geographic Information System, Digital Library System, etc. In this paper, we propose the semantic-based image retrieval (SBIR) system based on the deep learning technique; this system is called as SIR-DL that generates visual semantics based on classifying image contents. At the same time we identify the semantics of similar images on Ontology, which describes semantics of visual features of images. Firstly, the color and spatial features of segmented images are we extracted and these visual feature vectors are trained on the deep neural network to obtain visual words vectors. The process of image retrieval is executed rely on semantic classification of SIR-DL according to the visual feature vector of the query image from which it produces a visual word vector. Then, we retrieve it on Ontology to provide the identities and the semantics of similar images corresponds to a similarity measure. In order to carry out SIR-DL, the algorithms and diagram of this image retrieval system are proposed after that we implement them on ImageCLEF@IAPR, which has 20,000 images. On the base of the experimental results, the effectiveness of our method is evaluated by the accuracy, precision, recall, and F-measure; these results are compared with some of works recently published on the same image dataset. It shows that SIR-DL effectively solves the problem of semantic-based image retrieval and can be used to build multimedia systems in many different fields.

Keywords


bag of visual word; deep learning; ontology; SBIR; similarity measure; similar images

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References


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DOI: https://doi.org/10.15625/1813-9663/35/1/13097

Journal of Computer Science and Cybernetics ISSN: 1813-9663

Published by Vietnam Academy of Science and Technology