A Knowledge-Driven Multimedia Retrieval System Based on Semantics and Deep Features

Antonio Maria Rinaldi, Cristiano Russo and Cristian Tommasino
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Antonio Maria Rinaldi: Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio, 21, 80125 Napoli, Italy
Cristiano Russo: Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio, 21, 80125 Napoli, Italy
Cristian Tommasino: Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio, 21, 80125 Napoli, Italy

Future Internet, 2020, vol. 12, issue 11, 1-20

Abstract: In recent years the information user needs have been changed due to the heterogeneity of web contents which increasingly involve in multimedia contents. Although modern search engines provide visual queries, it is not easy to find systems that allow searching from a particular domain of interest and that perform such search by combining text and visual queries. Different approaches have been proposed during years and in the semantic research field many authors proposed techniques based on ontologies. On the other hand, in the context of image retrieval systems techniques based on deep learning have obtained excellent results. In this paper we presented novel approaches for image semantic retrieval and a possible combination for multimedia document analysis. Several results have been presented to show the performance of our approach compared with literature baselines.

Keywords: content base image retrieval; semantic information retrieval; deep features; multimedia document retrieval (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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