Abstract
The semantic gap between low-level features and high-level semantic concepts is a fundamental problem in content-based image retrieval (CBIR). To close this gap, relevant feedback is included in the CBIR. Based on the user’s feedback samples, a projection matrix is learned to project samples from the multi-dimensional original space to the low-dimensional projection space, and a classifier is learned on the projection space to classify the images. However, the number of classes in the relevance feedback is very small (only two classes), which leads to low classification performance and results in poor retrieval performance. To solve this problem, we propose a novel supervised image retrieval method, called Sparse Discriminant Analysis for Image Retrieval (SDAIR). Different from existing image retrieval methods, which have low precision due to small-class size problems, SDAIR is designed to not be affected by small-class size problems. Therefore, SDAIR is potentially more suitable for image retrieval with relevant feedback, where class sizes are often very small. The experimental results on the two databases demonstrate that the proposed method obtains competitive precision compared with other content-based image retrieval methods.
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References
Boyd S, Parikh N, Chu E, Peleato B, Eckstein J (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3(1):1–122
Chen W, Liu Y, Wang W, Bakker E, Georgiou T, Fieguth P, ..., Lew MS (2021) Deep image retrieval: A survey. arXiv preprint arXiv:2101.11282
Datta R, Joshi D, Li J, Wang JZ (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 40(2):1–60
Dorfer M, Kelz R, Widmer G (2015) Deep linear discriminant analysis. In: International Conference on Learning Representations, pp. 1–13
Dornaika F (2021) Multi-layer linear embedding with feature subset selection. Knowl Inf Syst 63(4):1029–1043
Dornaika F, Khoder A (2020) Linear embedding by joint Robust Discriminant Analysis and Inter-class Sparsity. Neural Netw 127:141–159
Duda RO, Hart PE, Stork DG (2000) Pattern Classification, 2nd edn. Wiley-Interscience
Duda RO, Hart PE, Stork DG (2012) Pattern classification. John Wiley & Sons
Fan Z, Xu Y, Zhang D (2011) Local linear discriminant analysis framework using sample neighbors. IEEE Trans Neural Netw 22(7):1119–1132
Hameed IM, Abdulhussain SH, Mahmmod BM (2021) Content-based image retrieval: A review of recent trends. Cogent Eng 8(1):1927469
Han N, Wu J, Liang Y, Fang X, Wong WK, Teng S (2018) Low-rank and sparse embedding for dimensionality reduction. Neural Netw 108:202–216
Hassan G, Hosny KM, Farouk RM, Alzohairy AM (2020a) Efficient Quaternion Moments for Representation and Retrieval of Biomedical Color Images. Biomed Eng: Appl Basis Commun 32(05):2050039
Hassan G, Hosny KM, Farouk RM, Alzohairy AM (2020b) An efficient retrieval system for biomedical images based on radial associated Laguerre moments. IEEE Access 8:175669–175687
Huijsmans DP, Sebe N (2005) How to complete performance graphs in content-based image retrieval: add generality and normalize scope. IEEE Trans Pattern Anal Mach Intell 27(2):245–251
Huu QN, Viet DC, Thuy QDT (2021) Semantic class discriminant projection for image retrieval with relevance feedback. Multimed Tools Appl 80(10):15351–15376
Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093
Jolliffe IT (2002) Principal Component Analysis, 2nd edn. Springer-Verlag, New-York
Khoder A, Dornaika F (2021) An enhanced approach to the robust discriminant analysis and class sparsity based embedding. Neural Netw 136:11–16
Kwak N, Choi C-H (2002) Input feature selection for classification problems. IEEE Trans Neural Netw 13(1):143–159
Lai Z, Xu Y, Jin Z, Zhang D (2014) Human gait recognition via sparse discriminant projection learning. IEEE Trans Circuits Syst Video Technol 24(10):1651–1662
Lai Z, Bao J, Kong H, Wan M, Yang G (2020) Discriminative low-rank projection for robust subspace learning. Int J Mach Learn Cybern 11:2247–2260
Li J, Allinson N, Tao D, Li X (2006) Multitraining support vector machine for image retrieval. IEEE Trans Image Process 15(11):3597–3601
Liu L, Yu M, Shao L (2015) Multiview alignment hashing for efficient image search. IEEE Trans Image Process 24(3):956–966
Liu Z, Liu G, Zhang L, Pu J (2020) Linear regression classification steered discriminative projection for dimension reduction. Multimed Tools Appl 79:11993–12005
Martinez AM, Kak AC (2002) Pca versus lda. IEEE Trans Pattern Anal Mach Intell 23(2):228–233
Sathiamoorthy S, Natarajan M (2020) An efficient content-based image retrieval using enhanced multi-trend structure descriptor. SN Appl Sci 2:217
Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380
Smith LI (2002) A tutorial on principal components analysis. Technical report
Stanczyk U, Zielosko B, Jain L (2018) Advances in feature selection for data and pattern recognition. Springer
Tao D, Tang X, Li X, Wu X (2006) Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans Pattern Anal Mach Intell 28(7):1088–1099
Tharwat A, Gaber T, Ibrahim A, Hassanien AE (2017) Linear discriminant analysis: A detailed tutorial. AI Commun 30(2):169–190
Wen J, Fang X, Cui J, Fei L, Yan K, Chen Y, Xu Y (2018) Robust sparse linear discriminant analysis. IEEE Trans Circuits Syst Video Technol 29:390–403
Wu L, Shen C, van den Hengel A (2017) Deep linear discriminant analysis on fisher networks: A hybrid architecture for person re-identification. Pattern Recogn 65:238–250
Xiang S, Nie F, Meng G, Pan C, Zhang C (2012) Discriminative least squares regression for multiclass classification and feature selection. IEEE Trans Neural Netw Learn Syst 23(11):1738–1754
Yan C, Chang X, Luo M, Zheng Q, Zhang X, Li Z, Nie F (2020) Self-weighted robust LDA for multiclass classification with edge classes. ACM Trans Intell Syst Technol (TIST) 12(1):1–19
Zhang L, Wang L, Lin W, Yan S (2014) Geometric optimum experimental design for collaborative image retrieval. IEEE Trans Circuits Syst Video Techn 24(2):346–359
Zhang L, Shum H, Shao L (2016) Discriminative semantic subspace analysis for relevance feedback. IEEE Trans Image Process 25(3):1275–1287
Zhou XS, Huang TS (2001) Small sample learning during multimedia retrieval using biasmap. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 11–17
Zhu R, Dornaika F, Ruichek Y (2019) Learning a discriminant graph-based embedding with feature selection for image categorization. Neural Netw 111:35–46
Zou H, Hastie T, Tibshirani R (2006) Sparse principal component analysis. J Comput Graph Stat 15(2):265–286
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This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.01-2020.10”.
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Hong, S.A., Huu, Q.N., Viet, D.C. et al. Improving image retrieval effectiveness via sparse discriminant analysis. Multimed Tools Appl 82, 30807–30830 (2023). https://doi.org/10.1007/s11042-023-14748-9
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DOI: https://doi.org/10.1007/s11042-023-14748-9