#6387. Content-based image retrieval using Group Normalized-Inception-Darknet-53

October 2026publication date
Proposal available till 22-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Library and Information Sciences;
Media Technology;
Information Systems;
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Abstract:
In recent days research, deep learning methods have shown promising performance in various fields of computer vision, including content-based image retrieval (CBIR). In this paper, an improved version of Darknet-53, called GroupNormalized-Inception-Darknet-53 (GN-Inception-Darknet-53), is proposed to extract features for the CBIR model. To extract the more detailed features of an image, we augmented one inception layer which includes 1 ? 1, 3 ? 3, and 5 ? 5 kernels in place of an existing 3 ? 3 kernel. The output of this newly added inception layer is the concatenated results of these three kernels. To make the normalization process of the proposed model less dependent on batch size, group normalization (GN) layer is used instead of batch normalization. A total of five such inception layers are used in the proposed GN-Inception-Darknet-53, and the output of all these inception layers is depth concatenated to extract more detailed features of the image. To train the proposed model transfer learning mechanism is used.
Keywords:
CBIR; Darknet-53; Deep learning; Group normalization; Inception layer; Transfer learning

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