Robust content-based image retrieval using ICCV, GLCM, and DWT-MSLBP descriptors

• Autorzy: Chavda Sagar , Goyani Mahesh

Identyfikatory

DOI

10.7494/csci.2022.23.1.3821

• Języki publikacji: EN

Abstrakty

EN

Content-based image retrieval (CBIR) retrieves visually similar images from a dataset based on a specified query. A CBIR system measures the similarities between a query and the image contents in a dataset and ranks the dataset images. This work presents a novel framework for retrieving similar images based on color and texture features. We have computed color features with an improved color coherence vector (ICCV) and texture features with a gray-level co-occurrence matrix (GLCM) along with DWT-MSLBP (which is derived from applying a modified multi-scale local binary pattern [MS-LBP] over a discrete wavelet transform [DWT], resulting in powerful textural features). The optimal features are computed with the help of principal component analysis (PCA) and linear discriminant analysis (LDA). The proposed work uses a variancebased approach for choosing the number of principal components/eigenvectors in PCA. PCA with a 99.99% variance preserves healthy features, and LDA selects robust ones from the set of features. The proposed method was tested on four benchmark datasets with Euclidean and city-block distances. The proposed method outshines all of the identified state-of-the-art literature methods.

Słowa kluczowe

EN

content-based image retrieval; improved color coherence vector; gray-level co-occurrence matrix; discrete wavelet transform; multi-scale local binary pattern; principal component analysis; linear discriminant analysis

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2022
• Tom: T. 23 (1)
• Strony: 5--36
• Opis fizyczny: Bibliogr. 97 poz., rys., tab.

Twórcy

autor

Chavda Sagar

• Gujarat Technological University, Ahmedabad, India
• Government Engineering College, Modasa, India

• https://orcid.org/0000-0002-7478-6814

autor

Goyani Mahesh

• Gujarat Technological University, Ahmedabad, India
• Government Engineering College, Modasa, India

• https://orcid.org/0000-0002-7269-1176

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Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).