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Combined Features for Content Based Image Retrieval: A Comparative Study

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018 (AISI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 845))

Abstract

Multimedia resources are rapidly growing with a huge increase of visual contents. Thus, searching these images accurately and efficiently for all types of datasets becomes one of the most challenging tasks. Content-based image retrieval (CBIR) is the technique that retrieves images based on their visual contents. So that, selecting appropriate features that describe an image sufficiently is a clue for a successful retrieval system. To this end, in this paper, a comparative study to investigate the effect of using a single and a combined set of features in the context of a CBIR is presented. To achieve this goal, several features including, edge histogram (EHD), color layout (CLD) and fuzzy color texture histogram (FCTH) as well as different combinations of these features such as, all edges (local, global and semi-global edges), all edges with CLD and finally, all edges with FCTH have been exploited. To demonstrate the effectiveness of the proposed method, a set of experiments utilizing different images datasets have been carried out. The results in terms of precision, recall, F-measure and mean average precision show a higher retrieval accuracy while using a set of combined features compared to exploiting only single features for the same retrieval task.

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Notes

  1. 1.

    http://www.image-net.org/api/text/imagenet.synset.geturls?wnid=.

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Acknowledgments

This work was done at the SWEP course as a part of the BioDialog project which is funded by DAAD at Friedrich Schiller University of Jena. Special thanks Prof. Birgitta König-Ries for her support.

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Authors and Affiliations

  1. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Nora Youssef, Ibrahim F. Moawad & EL-Sayed M. EL-Horbaty

  2. Institute of Computer Science, Friedrich Schiller University of Jena, Jena, Germany

    Alsayed Algergawy

Authors
  1. Nora Youssef
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  2. Alsayed Algergawy
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  3. Ibrahim F. Moawad
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  4. EL-Sayed M. EL-Horbaty
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Corresponding author

Correspondence to Nora Youssef .

Editor information

Editors and Affiliations

  1. Information Technology Department, Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

  3. Dubai International Academic City, The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  4. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

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Youssef, N., Algergawy, A., Moawad, I.F., EL-Horbaty, ES.M. (2019). Combined Features for Content Based Image Retrieval: A Comparative Study. In: Hassanien, A., Tolba, M., Shaalan, K., Azar, A. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018. AISI 2018. Advances in Intelligent Systems and Computing, vol 845. Springer, Cham. https://doi.org/10.1007/978-3-319-99010-1_58

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