A framework for content based fine-grained image retrieval (CB-FGIR) by using CNN is presented in [6]. More info can be found in the paper.
Results for Oxford Flower 17 dataset
| Method | MAP |
|---|---|
| LBP | 0.102 |
| HOG | 0.110 |
| Yang et al. [1] (Vgg-16) | 0.8772 |
| ResNet18(Pretrained) | 0.519 |
| Resnet-18(Pretrained + fine-tuned) | 0.928 |
Results for cars-196 dataset
| Method | SPOC [2]* | CroW [3]* | R-MAC [4]* | Wei et al. [5] | Resnet-18 (Pretrained+FineTuned) |
|---|---|---|---|---|---|
| Dimension | 256 | 256 | 512 | 512 | 512 |
| Top1 MAP | 0.2986 | 0.4492 | 0.4654 | 0.5330 | 0.84 |
| Top2 MAP | 0.3623 | 0.5118 | 0.5298 | 0.5911 | 0.80 |
[1] H. Yang, K. Lin, and C. Chen. "Cross-batch reference learning for deep classification and retrieval." In Proceedings of the 24th ACM international conference on Multimedia, 2016, pp. 1237-1246.
[2] A. B. Yandex and V. Lempitsky, "Aggregating Local Deep Features for Image Retrieval," 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp. 1269-1277
[3] Y. Kalantidis, C. Mellina, and S. Osindero. "Cross-dimensional weighting for aggregated deep convolutional features." In European
conference on computer vision, Springer, Cham, 2016, pp. 685-701.
[4] G. Tolias, R. Sicre, and H. Jégou. "Particular object retrieval with integral max-pooling of CNN activations." arXiv preprint arXiv:1511.05879, 2015
[5] X. Wei, J. Luo, J. Wu and Z. Zhou, "Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval," in IEEE Transactions on Image Processing, vol. 26, no. 6, pp. 2868-2881, June 2017.
If you find it useful, kindly cite this paper:
[6] V. Kumar, V. Tripathi and B. Pant, "Content based Fine-Grained Image Retrieval using Convolutional Neural Network," 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 2020, pp. 1120-1125.