Attributes Grouping and Mining Hashing for Fine-Grained Image Retrieval

Abstract

In recent years, hashing methods have been popular in the largescale media search for low storage and strong representation capabilities. To describe objects with similar overall appearance but subtle differences, more and more studies focus on hashing-based fine-grained image retrieval. Existing hashing networks usually generate both local and global features through attention guidance on the same deep activation tensor, which limits the diversity of feature representations. To handle this limitation, we substitute convolutional descriptors for attention-guided features and propose an Attributes Grouping and Mining Hashing (AGMH), which groups and embeds the category-specific visual attributes in multiple descriptors to generate a comprehensive feature representation for efficient fine-grained image retrieval. Specifically, an Attention Dispersion Loss (ADL) is designed to force the descriptors to attend to various local regions and capture diverse subtle details. Moreover, we propose a Stepwise Interactive External Attention (SIEA) to mine critical attributes in each descriptor and construct correlations between fine-grained attributes and objects. The attention mechanism is dedicated to learning discrete attributes, which will not cost additional computations in hash codes generation. Finally, the compact binary codes are learned by preserving pairwise similarities. Experimental results demonstrate that AGMH consistently

Multi-FusNet: fusion mapping of features for fine-grained image retrieval networks

“…Comparison with the SOTA methods: In our experimental setup, we utilised seven well-established approaches (namely DPSH ( Li, Wang & Kang, 2016 ), HashNet ( Cao et al, 2017 ), ADSH ( Jiang & Li, 2018 ), ExchNet ( Cui et al, 2020 ), AA-Net ( Chen et al, 2018 ), SEMICON ( Shen et al, 2022 ) and AMGH ( Lu et al, 2023 )) as benchmarks to showcase the effectiveness of our suggested approach. In this part, Table 3 displays the mean average precision (mAP) for the five publicly available datasets discussed in ‘Datasets and evaluation metric’ section.…”

“…Afterwards, an improved graph convolutional network (GCN) is used to model the internal semantic interactions of the learned local features to obtain a more discriminative and fine-grained image representation ( Li & Ma, 2023 ). Later others replaced attention-guided features with convolutional descriptors and proposed an Attribute Searching and Mining Hash (AGMH), which groups category specific visual attributes and embeds them into multiple descriptors to generate a comprehensive feature representation for efficient fine-grained image retrieval ( Lu et al, 2023 ). Later Duan et al (2022) computed similarity matrices at channel, pixel, and spatial levels by acquiring deep localized descriptors.…”

“…The above methods have made significant contributions to the field of fine-grained image retrieval. However, their main goal is still focusing on the localization of the target’s key regions and incorrectly associated features with the number of valid bits of the abbreviated hash code, which lead to the limitation of the retrieval efficiency and becamde a key issue that needs to be paid attention to ( Lu et al., 2023 ). This study draws inspiration from the Transformer model architecture to effectively capture the complex details of the overall context.…”