Research article

Fruit-Fly optimization based feature integration in image retrieval

  • Received: 07 April 2021 Accepted: 08 June 2021 Published: 14 July 2021
  • The content-based image retrieval (CBIR) system searches and retrieves the similar images from the huge database using the significant features extracted from the image. Feature integration techniques used in the CBIR system assign static weights to each feature involved in the retrieval process that gives a smaller number of similar images as a result. Moreover, the retrieval time of the CBIR system increases due to the entire database search. To overcome this disadvantage the proposed work introduced a two-level searching process in the CBIR system. The initial level of the proposed framework uses the image selection rule to select more relevant images for the second-level process. The second level of the framework takes the proposed dominant color and radial difference pattern details from the query and selected images. By using color and texture features of the selected images, similarity measure is calculated. The proposed work assigns optimal dynamic weight to the similarity measure of color and texture features using the fruit fly optimization algorithm. This improves the retrieval performance of the CBIR system.

    Citation: Pavithra Latha Kumaresan, Subbulakshmi Pasupathi, Sindhia Lingaswamy, Sreesharmila Thangaswamy, Vimal Shunmuganathan, Danilo Pelusi. Fruit-Fly optimization based feature integration in image retrieval[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 6178-6197. doi: 10.3934/mbe.2021309

    Related Papers:

  • The content-based image retrieval (CBIR) system searches and retrieves the similar images from the huge database using the significant features extracted from the image. Feature integration techniques used in the CBIR system assign static weights to each feature involved in the retrieval process that gives a smaller number of similar images as a result. Moreover, the retrieval time of the CBIR system increases due to the entire database search. To overcome this disadvantage the proposed work introduced a two-level searching process in the CBIR system. The initial level of the proposed framework uses the image selection rule to select more relevant images for the second-level process. The second level of the framework takes the proposed dominant color and radial difference pattern details from the query and selected images. By using color and texture features of the selected images, similarity measure is calculated. The proposed work assigns optimal dynamic weight to the similarity measure of color and texture features using the fruit fly optimization algorithm. This improves the retrieval performance of the CBIR system.



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