CITATION.cff

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  Content-Based Image Retrieval system based on Bayesian
  Relevance Feedback
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Ahmed
    family-names: Samady
    email: ahmed.samady@etu.uae.ac.ma
  - given-names: Fahd
    family-names: Chibani
    email: fahd.chibani@etu.uae.ac.ma
  - given-names: Mohamed Amine
    family-names: Fakhre-Eddine
    email: contact@fakhreeddine.dev
repository-code: >-
  https://github.com/Samashi47/content-based-image-retrieval/
abstract: >-
  The theory, design principles, implementation, and
  performance outcomes of our two-week-long content-based
  image retrieval (CBIR) system are presented in this paper.
  Furthermore, we suggest a Bayesian Inference-based
  relevance feedback method. In order to achieve this, we
  use labels to determine whether a query is relevant. We
  then use a Bernoulli likelihood model, a Gaussian model,
  and Markov Chain Monte Carlo (MCMC) sampling in order, to
  update the weights of each feature, which improves and
  refines the results of a user query. Lastly, we
  demonstrated our implementation validating the theoretical
  conclusions.
keywords:
  - Content-Based Image Retrieval (CBIR)
  - Bayesian Inference
  - Relevance Feedback
  - Image Search
license: MIT
date-released: "2024-12-15"