Pradhan J, Ajad A, Pal AK, Banka H. Multi-level colored directional motif histograms for content-based image retrieval. Vis Comput. 2020;36(9):1847–68. https://doi.org/10.1007/s00371-019-01773-9.

Article  Google Scholar 

Xiong ZG, Tang ZW, Chen XW, Zhang XM, Zhang KB, Ye CH. Research on image retrieval algorithm based on combination of color and shape features. J Signal Process Syst. 2021;93:139–46. https://doi.org/10.1007/s11265-019-01508-y.

Article  MATH  Google Scholar 

Zhang Z, Wang L, Wang Y, Zhou L, Zhang J, Chen F. Dataset-driven unsupervised object discovery for region-based instance image retrieval. IEEE Trans Pattern Anal Mach Intell. 2023;45(1):247–63. https://doi.org/10.1109/TPAMI.2022.3141433.

Article  MATH  Google Scholar 

Zhou J, Gan J, Gao W, Liang A. Image retrieval based on aggregated deep features weighted by regional significance and channel sensitivity. Inform Sci. 2021;577:69–80. https://doi.org/10.1016/j.ins.2021.06.002.

Article  MathSciNet  MATH  Google Scholar 

Liu GH, Yang JY. Deep-seated features histogram: a novel image retrieval method. Pattern Recogn. 2021;116:107926. https://doi.org/10.1016/j.patcog.2021.107926.

Article  Google Scholar 

Hubel DH, Wiesel TN. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol. 1962;160(1):106–54. https://doi.org/10.1113/jphysiol.1962.sp006837.

Article  MATH  Google Scholar 

Liu GH, Yang JY. Exploiting color volume and color difference for salient region detection. IEEE Trans Image Process. 2019;28(1):6–16. https://doi.org/10.1109/TIP.2018.2847422.

Article  MathSciNet  MATH  Google Scholar 

Adelson EH, Bergen JR. The plenoptic function and the elements of early vision. Comput Models Vis Process. 1991;1(2):3–20.

MATH  Google Scholar 

Liu GH, Yang JY, Li ZY. Content-based image retrieval using computational visual attention model. Pattern Recogn. 2015;48(8):2554–66. https://doi.org/10.1016/j.patcog.2015.02.005.

Article  MATH  Google Scholar 

Srivastava D, Wadhvani R, Gyanchandani M. A review: color feature extraction methods for content-based image retrieval. Int J Comput Eng Manag. 2015;18(3):9–13.

MATH  Google Scholar 

Hung CC, Song E, Lan Y. Image texture analysis, Springer, Cham, 2019; https://doi.org/10.1007/978-3-030-13773-1.

Jiebo Luo, Crandall D. Color object detection using spatial-color joint probability functions. IEEE Trans Image Process. 2006;15(6):1443–53. https://doi.org/10.1109/TIP.2006.871081.

Article  MATH  Google Scholar 

Liu GH, Li ZY, Zhang L, Xu Y. Image retrieval based on micro-structure descriptor. Pattern Recognit. 2011;44(9):2123–33. https://doi.org/10.1016/j.patcog.2011.02.003.

Article  MATH  Google Scholar 

Zhang D, Lu G. Review of shape representation and description techniques. Pattern Recogn. 2004;37(1):1–19. https://doi.org/10.1016/j.patcog.2003.07.008.

Article  MATH  Google Scholar 

Yap PT, Paramesran R, Ong SH. Image analysis using Hahn moments. IEEE Trans Pattern Anal Mach Intell. 2007;29(11):2057–62. https://doi.org/10.1109/TPAMI.2007.70709.

Article  MATH  Google Scholar 

Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vision. 2004;60:91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94.

Article  MATH  Google Scholar 

Bay H, Ess A, Tuytelaars T, Gool LV. Speeded-up robust features (SURF). Comput Vis Image Underst. 2008;110(3):346–59. https://doi.org/10.1016/j.cviu.2007.09.014.

Article  Google Scholar 

Zheng L, Yang Y, Tian Q. SIFT meets CNN: a decade survey of instance retrieval. IEEE Trans Pattern Anal Mach Intell. 2018;40(5):1224–44. https://doi.org/10.1109/TPAMI.2017.2709749.

Article  MATH  Google Scholar 

Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25. https://doi.org/10.1145/3065386.

Kalantidis Y, Mellina C, Osindero S. Cross-dimensional weighting for aggregated deep convolutional features. Proceedings of the European Conference on Computer Vision, Springer, Cham, 2016, pp. 685–701; https://doi.org/10.1007/978-3-319-46604-0_48.

Zhu J, Wang J, Pang S, Guan W, Li Z, Li Y, et al. Co-weighting semantic convolutional features for object retrieval. J Vis Commun Image Represent. 2019;62:368–80. https://doi.org/10.1016/j.jvcir.2019.06.006.

Article  MATH  Google Scholar 

Pang S, Zhu J, Wang J, Ordonez V, Xue J. Building discriminative CNN image representations for object retrieval using the replicator equation. Pattern Recogn. 2018;83:150–60. https://doi.org/10.1016/j.patcog.2018.05.010.

Article  Google Scholar 

Li Y, Kong X, Zheng L, Tian Q. Exploiting hierarchical activations of neural network for image retrieval. Proceedings of the 24th ACM International Conference on Multimedia, 2016, pp. 132–136; https://doi.org/10.1145/2964284.2967197.

Liu GH, Yang JY. Exploiting deep textures for image retrieval. Int J Mach Learn Cybern. Feb.2023;14(2):483–94. https://doi.org/10.1007/s13042-022-01645-0.

Article  MATH  Google Scholar 

Lu Z, Liu GH, Lu F, Zhang BJ. Image retrieval using dual-weighted deep feature descriptor. Int J Mach Learn Cybern. 2023;14(3):643–53. https://doi.org/10.1007/s13042-022-01654-z.

Article  MATH  Google Scholar 

Liu GH, Li ZY, Yang JY, Zhang D. Exploiting sublimated deep features for image retrieval. Pattern Recogn. 2024;147:110076. https://doi.org/10.1016/j.patcog.2023.110076.

Article  MATH  Google Scholar 

Arandjelović R, Gronat P, Torii A, Pajdla T, Sivic J. NetVLAD: CNN architecture for weakly supervised place recognition. IEEE Trans Pattern Anal Mach Intell. 2018;40(6):1437–51. https://doi.org/10.1109/TPAMI.2017.2711011.

Article  MATH  Google Scholar 

Radenović F, Tolias G, Chum O. Fine-tuning CNN image retrieval with no human annotation. IEEE Trans Pattern Anal Mach Intell. 2019;41(7):1655–68. https://doi.org/10.1109/TPAMI.2018.2846566.

Article  Google Scholar 

Xu Y, Shamsolmoali P, Granger E, Nicodeme C, Gardes L, Yang J. TransVLAD: multi-scale attention-based global descriptors for visual geo-localization. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2023, pp. 2839–2848; https://doi.org/10.1109/WACV56688.2023.00286.

Caron M, Touvron H, Misra I, Jegou H, Mairal J, Bojanowski P, et al. Emerging properties in self-supervised vision transformers. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 2021, pp. 9630–9640; https://doi.org/10.1109/ICCV48922.2021.00951.

Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, et al. Learning transferable visual models from natural language supervision. In: International conference on machine learning. PMLR; 2021. pp. 8748–63. https://proceedings.mlr.press/v139/radford21a/radford21a.pdf.

Li J, Li D, Xiong C, Hoi S. Blip: bootstrapping language-image pre-training for unified vision-language understanding and generation. In: International conference on machine learning. PMLR. 2022;12888–12900. https://proceedings.mlr.press/v162/li22n/li22n.pdf.

Geirhos R, Rubisch P, Michaelis C, Bethge M, Wichmann FA, Brendel W. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In: International conference on learning representations. 2018. https://openreview.net/forum?id=Bygh9j09KX.

Meester MJ, Baslamisli AS. SAR image edge detection: review and benchmark experiments. Int J Remote Sens. 2022;43(14):5372–438. https://doi.org/10.1080/01431161.2022.2131480.

Article  MATH  Google Scholar 

Xu J, Wang C, Qi C, Shi C, Xiao B. Unsupervised semantic-based aggregation of deep convolutional features. IEEE Trans Image Process. 2019;28(2):601–11. https://doi.org/10.1109/TIP.2018.2867104.

Article  MathSciNet  MATH  Google Scholar 

UFLDL Tutorial. PCA whitening. http://ufdl.stanford.edu/tutorial/unsupervised/PCAWhitening/. Accessed 25 Sep 2022.

Lu F, Liu GH. Image retrieval using object semantic aggregation histogram. Cognitive Computation, pp. 1–12, 2023; https://doi.org/10.1007/s12559-023-10143-6.

Philbin J, Chum O, Isard M, Sivic J, Zisserman A. Object retrieval with large vocabularies and fast spatial matching. 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, 2007, pp. 1–8; https://doi.org/10.1109/CVPR.2007.383172.

Philbin J, Chum O, Isard M, Sivic J, Zisserman A. Lost in quantization: improving particular object retrieval in large scale image databases. 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, 2008, pp. 1–8; https://doi.org/10.1109/CVPR.2008.4587635.

Radenovic F, Iscen A, Tolias G, Avrithis Y, Chum O. Revisiting Oxford and Paris: large-scale image retrieval benchmarking. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 5706–5715; https://doi.org/10.1109/CVPR.2018.00598.

Jégou H, Douze M, Schmid C. Hamming embedding and weak geometric consistency for large scale image search. Computer Vision – ECCV, vol. 5302, pp. 304–317, 2008; https://doi.org/10.1007/978-3-540-88682-2_24.

Pang S, Ma J, Xue J, Zhu J, Ordonez V. Deep feature aggregation and image re-ranking with heat diffusion for image retrieval. IEEE Trans Multimedia. 2019;21(6):1513–23. https://doi.org/10.1109/TMM.2018.2876833.

Article  MATH  Google Scholar 

Forcén JI, Pagola M, Barrenechea E, Bustince H. Co-occurrence of deep convolutional features for image search. Image Vis Comput. 2020;97:103909. https://doi.org/10.1016/j.imavis.2020.103909.

Article  MATH  Google Scholar 

Zhang B, Wang Q, Lu X, Wang F, Li P. Locality-constrained affine subspace coding for image classification and retrieval. Pattern Recogn. 2020;100:107167. https://doi.org/10.1016/j.patcog.2019.107167.

Article  MATH  Google Scholar 

Bai C, Li H, Zhang J, Huang L, Zhang L. Unsupervised adversarial instance-level image retrieval. IEEE Trans Multimedia. 2021;23:2199–207. https://doi.org/10.1109/TMM.2021.3065578.

Article  MATH