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Image Retrieval Based on MPEG-7 Feature Selection Using Meta-heuristic Algorithms

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Digital Technologies and Applications (ICDTA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 211))

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Abstract

The continuous growth of the internet and its diversified domains and tools; and the rapid development of new technologies have enormously increased the amount of digital images and consequently the dimension of the image data sets. Therefore, finding the relevant images to a query image in such database is a challenging task. Content-based image retrieval is the powerful method so far which use the visual characteristics of the image. The choice of these visual descriptors is a decisive phase in this area. This work presents a method which extracts the three image features which are: color, texture and shape based on the MPEG-7 standards. Then, the genetic algorithm is implemented for feature selection to provide the best extracted features to avoid unnecessary ones and reduce calculations and retrieval time. Meanwhile, the k-nearest neighbors algorithm is used to search for the relevant images. Finally, we will apply our method on two different image data-sets which contain images belonging to different domains to show the efficiency of selecting the correct features using meta-heuristic algorithms.

MATSI Laboratory, ESTO, University of Mohammed Premier, Oujda, Morocco

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Authors and Affiliations

  1. Laboratory MATSI, ESTO, University of Mohammed Premier, Oujda, Morocco

    Naoufal Machhour & M’barek Nasri

Authors
  1. Naoufal Machhour
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  2. M’barek Nasri
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Corresponding author

Correspondence to Naoufal Machhour .

Editor information

Editors and Affiliations

  1. ENSA, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Saad Motahhir

  2. Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Badre Bossoufi

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Machhour, N., Nasri, M. (2021). Image Retrieval Based on MPEG-7 Feature Selection Using Meta-heuristic Algorithms. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2021. Lecture Notes in Networks and Systems, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-030-73882-2_80

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