A new approach to descriptors generation for image retrieval by analyzing activations of deep neural network layers
Authors:
- Paweł Staszewski,
- Maciej Jaworski,
- Jinde Cao,
- Leszek Rutkowski
Abstract
In this brief, we consider the problem of descriptors construction for the task of content-based image retrieval using deep neural networks. The idea of neural codes, based on fully connected layers' activations, is extended by incorporating the information contained in convolutional layers. It is known that the total number of neurons in the convolutional part of the network is large and the majority of them have little influence on the final classification decision. Therefore, in this brief, we propose a novel algorithm that allows us to extract the most significant neuron activations and utilize this information to construct effective descriptors. The descriptors consisting of values taken from both the fully connected and convolutional layers perfectly represent the whole image content. The images retrieved using these descriptors match semantically very well to the query image, and also, they are similar in other secondary image characteristics, such as background, textures, or color distribution. These features of the proposed descriptors are verified experimentally based on the IMAGENET1M dataset using the VGG16 neural network. For comparison, we also test the proposed approach on the ResNet50 network.
- Record ID
- CUTb2d4a00b04e24b7ba986a8ad077031a2
- Publication categories
- ;
- Author
- Journal series
- IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, e-ISSN 2162-2388
- Issue year
- 2022
- Vol
- 33
- No
- 12
- Pages
- 7913-7920
- Other elements of collation
- fot.; rys.; tab.; wykr.; Bibliografia (na s.) - 7920; Bibliografia (liczba pozycji) - 31; Oznaczenie streszczenia - Abstr.; Data udostępnienia on-line - 2021-06-10; Numeracja w czasopiśmie - Vol. 33, Iss. 12
- Keywords in English
- content-based image retrieval (CBIR), convolution neural networks, deep learning, image processing, image retrieval
- ASJC Classification
- ; ; ;
- DOI
- DOI:10.1109/TNNLS.2021.3084633 Opening in a new tab
- URL
- https://ieeexplore.ieee.org/document/9451541 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 200
- Score source
- journalList
- Score
- Publication indicators
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTb2d4a00b04e24b7ba986a8ad077031a2/
- URN
urn:pkr-prod:CUTb2d4a00b04e24b7ba986a8ad077031a2
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.