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Semantic Image Analysis for Automatic Image Annotation

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Intelligent Systems in Big Data, Semantic Web and Machine Learning

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1344))

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Abstract

The semantic image analysis is a process aimed to extract automatically the accurate semantic concept from image visual content. In this chapter, we shed light on a study conducted to boost the efficiency of semantic image analysis for improving automatic image annotation. We have explored, in this research study, different aspects of semantic analysis to overcome the semantic gap problem. In the first part of this research study, we evaluated the impact of the way in which the different low level features were associated on image annotation performance. In the second part, the benefit of combining two complementary classifiers for objects classification was investigated. The final part of the research study was devoted to the assessment of the usefulness of regrouping adjacent regions of segmented image for objects discrimination.

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Author information

Authors and Affiliations

  1. Laboratory of Information Processing and Decision Making, Department of Physics, Faculty of Science and Technology, Sultan Moulay Slimane University, Mghila, PO Box. 523, Beni Mellal, Morocco

    Brahim Minaoui

  2. Laboratory of Informatics, Mathematics and Communication Systems, Departement of Computer Science, Networks and Telecoms, National School of Applied Sciences, Cadi Ayyad University, Road Sidi Bouzid, PO Box. 63, Safi, Morocco

    Mustapha Oujaoura

Authors
  1. Brahim Minaoui
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  2. Mustapha Oujaoura
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Editor information

Editors and Affiliations

  1. ENSA, Sultan Moulay Slimane University, Khouribga, Morocco

    Noreddine Gherabi

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Minaoui, B., Oujaoura, M. (2021). Semantic Image Analysis for Automatic Image Annotation. In: Gherabi, N., Kacprzyk, J. (eds) Intelligent Systems in Big Data, Semantic Web and Machine Learning. Advances in Intelligent Systems and Computing, vol 1344. Springer, Cham. https://doi.org/10.1007/978-3-030-72588-4_4

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