IEICE TRANSACTIONS on Information
Learning Local Similarity with Spatial Interrelations on Content-Based Image Retrieval
Summary :
Convolutional Neural Networks (CNNs) have recently demonstrated outstanding performance in image retrieval tasks. Local convolutional features extracted by CNNs, in particular, show exceptional capability in discrimination. Recent research in this field has concentrated on pooling methods that incorporate local features into global features and assess the global similarity of two images. However, the pooling methods sacrifice the image's local region information and spatial relationships, which are precisely known as the keys to the robustness against occlusion and viewpoint changes. In this paper, instead of pooling methods, we propose an alternative method based on local similarity, determined by directly using local convolutional features. Specifically, we first define three forms of local similarity tensors (LSTs), which take into account information about local regions as well as spatial relationships between them. We then construct a similarity CNN model (SCNN) based on LSTs to assess the similarity between the query and gallery images. The ideal configuration of our method is sought through thorough experiments from three perspectives: local region size, local region content, and spatial relationships between local regions. The experimental results on a modified open dataset (where query images are limited to occluded ones) confirm that the proposed method outperforms the pooling methods because of robustness enhancement. Furthermore, testing on three public retrieval datasets shows that combining LSTs with conventional pooling methods achieves the best results.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.5 pp.1069-1080
- Publication Date
- 2023/05/01
- Publicized
- 2023/02/14
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022EDP7163
- Type of Manuscript
- PAPER
- Category
- Image Processing and Video Processing
Authors
Longjiao ZHAO
Nagoya University
Yu WANG
Hitotsubashi University
Jien KATO
Ritsumeikan University
Yoshiharu ISHIKAWA
Nagoya University
Keyword
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Longjiao ZHAO, Yu WANG, Jien KATO, Yoshiharu ISHIKAWA, "Learning Local Similarity with Spatial Interrelations on Content-Based Image Retrieval" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 5, pp. 1069-1080, May 2023, doi: 10.1587/transinf.2022EDP7163.
Abstract: Convolutional Neural Networks (CNNs) have recently demonstrated outstanding performance in image retrieval tasks. Local convolutional features extracted by CNNs, in particular, show exceptional capability in discrimination. Recent research in this field has concentrated on pooling methods that incorporate local features into global features and assess the global similarity of two images. However, the pooling methods sacrifice the image's local region information and spatial relationships, which are precisely known as the keys to the robustness against occlusion and viewpoint changes. In this paper, instead of pooling methods, we propose an alternative method based on local similarity, determined by directly using local convolutional features. Specifically, we first define three forms of local similarity tensors (LSTs), which take into account information about local regions as well as spatial relationships between them. We then construct a similarity CNN model (SCNN) based on LSTs to assess the similarity between the query and gallery images. The ideal configuration of our method is sought through thorough experiments from three perspectives: local region size, local region content, and spatial relationships between local regions. The experimental results on a modified open dataset (where query images are limited to occluded ones) confirm that the proposed method outperforms the pooling methods because of robustness enhancement. Furthermore, testing on three public retrieval datasets shows that combining LSTs with conventional pooling methods achieves the best results.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDP7163/_p
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@ARTICLE{e106-d_5_1069,
author={Longjiao ZHAO, Yu WANG, Jien KATO, Yoshiharu ISHIKAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Learning Local Similarity with Spatial Interrelations on Content-Based Image Retrieval},
year={2023},
volume={E106-D},
number={5},
pages={1069-1080},
abstract={Convolutional Neural Networks (CNNs) have recently demonstrated outstanding performance in image retrieval tasks. Local convolutional features extracted by CNNs, in particular, show exceptional capability in discrimination. Recent research in this field has concentrated on pooling methods that incorporate local features into global features and assess the global similarity of two images. However, the pooling methods sacrifice the image's local region information and spatial relationships, which are precisely known as the keys to the robustness against occlusion and viewpoint changes. In this paper, instead of pooling methods, we propose an alternative method based on local similarity, determined by directly using local convolutional features. Specifically, we first define three forms of local similarity tensors (LSTs), which take into account information about local regions as well as spatial relationships between them. We then construct a similarity CNN model (SCNN) based on LSTs to assess the similarity between the query and gallery images. The ideal configuration of our method is sought through thorough experiments from three perspectives: local region size, local region content, and spatial relationships between local regions. The experimental results on a modified open dataset (where query images are limited to occluded ones) confirm that the proposed method outperforms the pooling methods because of robustness enhancement. Furthermore, testing on three public retrieval datasets shows that combining LSTs with conventional pooling methods achieves the best results.},
keywords={},
doi={10.1587/transinf.2022EDP7163},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Learning Local Similarity with Spatial Interrelations on Content-Based Image Retrieval
T2 - IEICE TRANSACTIONS on Information
SP - 1069
EP - 1080
AU - Longjiao ZHAO
AU - Yu WANG
AU - Jien KATO
AU - Yoshiharu ISHIKAWA
PY - 2023
DO - 10.1587/transinf.2022EDP7163
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2023
AB - Convolutional Neural Networks (CNNs) have recently demonstrated outstanding performance in image retrieval tasks. Local convolutional features extracted by CNNs, in particular, show exceptional capability in discrimination. Recent research in this field has concentrated on pooling methods that incorporate local features into global features and assess the global similarity of two images. However, the pooling methods sacrifice the image's local region information and spatial relationships, which are precisely known as the keys to the robustness against occlusion and viewpoint changes. In this paper, instead of pooling methods, we propose an alternative method based on local similarity, determined by directly using local convolutional features. Specifically, we first define three forms of local similarity tensors (LSTs), which take into account information about local regions as well as spatial relationships between them. We then construct a similarity CNN model (SCNN) based on LSTs to assess the similarity between the query and gallery images. The ideal configuration of our method is sought through thorough experiments from three perspectives: local region size, local region content, and spatial relationships between local regions. The experimental results on a modified open dataset (where query images are limited to occluded ones) confirm that the proposed method outperforms the pooling methods because of robustness enhancement. Furthermore, testing on three public retrieval datasets shows that combining LSTs with conventional pooling methods achieves the best results.
ER -
English
