Discriminative local features obtained from activations of convolutional neural networks have proven to be essential for image retrieval.

In this paper, we combine global and local features in different scales to improve discriminative performance in fine-grained image retrieval tasks.

In this paper, we propose a novel graph-based discriminative features learning network for fine-grained image retrieval (GDF-Net). We first design a global fine ...

Abstract: This paper proposes a CNN-based retrieval framework that uses Siamese network to learn a CNN model for image feature extraction.

Discriminative local features obtained from activations of convolutional neural networks have proven to be essential for image retrieval.

Sep 20, 2024 · Therefore, the primary objective was to efficiently develop and train a universal image encoder capable of extracting discriminative image ...

It can be used for both fine-grained recognition and retrieval of firearm images. Recent advances in image retrieval are mainly driven by fine-tuning state-of- ...

Apr 23, 2023 · Bibliographic details on Learning Discriminative Features for Image Retrieval.

This paper proposes a CNN-based retrieval framework that uses Siamese network to learn a CNN model for image feature extraction. Model training and testing ...

Mar 6, 2024 · Image retrieval can evaluate the ability of extractors to capture the most relevant information of images without label intervention. Therefore, ...