An improved facial image retrieval using hybrid cheetah optimization algorithm

• Autorzy: Balasubramanian C. , Raja Sekar J.

Identyfikatory

DOI

10.24425/bpasts.2024.148942

• Języki publikacji: EN

Abstrakty

EN

With the advent of social media, the volume of photographs uploaded on the internet has increased exponentially. The task of efficiently recognizing and retrieving human facial images is inevitable and essential at this time. In this work, a feature selection approach for recognizing and retrieving human face images using hybrid cheetah optimization algorithm is proposed. The deep feature extraction from the images is done using deep convolutional neural networks. Hybrid cheetah optimization algorithm, an improvised version of cheetah optimization algorithm fused with genetic algorithm is used, to choose optimum features from the extracted deep features. The chosen features are used for finding the best-matching images from the image database. The image matching is performed by approximate nearest neighbor search for the query image over the image database and similar images are retrieved. By constructing a k-NN graph for the images, the efficiency of image retrieval is enhanced. The proposed system performance is evaluated against benchmark datasets such as LFW, MultiePie, ColorFERET, DigiFace-1M and CelebA. The evaluation results show that the proposed methodology is superior to various existing methodologies.

Słowa kluczowe

EN

deep learning; feature selection; facial image retrieval; cheetah optimization; approximate nearest neighbor

PL

głęboka nauka; wybór funkcji; pobieranie obrazu twarzy; algorytm optymalizacji geparda; wyszukiwanie najbliższego sąsiada

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2024
• Tom: Vol. 72, nr 2
• Strony: art. no. e148942
• Opis fizyczny: Bibliogr 44 poz., rys., tab.

Twórcy

autor

Balasubramanian C.

• Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi 626005, India

• https://orcid.org/0000-0003-4316-0867

autor

Raja Sekar J.

• Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi 626005, India

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).