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.
Highlights
The main aim of a search engine is to satisfy user information needs [1] retrieving relevant information for the user [2]
We propose a novel framework for multimedia web document retrieval system combining semantic similarity measures based on a formal and semantic multimedia knowledge base and different image descriptors
We use the first dataset to evaluate and select retrieval metrics based on semantic similarity, the second one to evaluate and select the deep features in content-based image retrieval systems (CBIRs) context and the last one to perform the final tests in which we combine the different strategies
Summary
The main aim of a search engine is to satisfy user information needs [1] retrieving relevant information for the user [2]. A sign cannot be limited to words, and images, sounds, videos and more Starting form these considerations, the design of a modern information retrieval system takes into account that information can be represented in different forms, in order to improve the efficiency and effectiveness of the whole retrieval process. The main effect of the semantic gap is that a query expresses in terms of low-level features can return wrong results if the conceptual content is not given. For this reason, the representation of low-level features is a crucial task in information retrieval systems.
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