Document Type : Original/Review Paper


1 Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran.

2 Faculty of Computer Engineering, Shahid Beheshti University, Tehran, Iran.



Image retrieval is a basic task in many content-based image systems. Achieving high precision, while maintaining computation time is very important in relevance feedback-based image retrieval systems. This paper establishes an analogy between this and the task of image classification. Therefore, in the image retrieval problem, we will obtain an optimized decision surface that separates dataset images into two categories of relevant/irrelevant images corresponding to the query image. This problem will be viewed and solved as an optimization problem using particle optimization algorithm. Although the particle swarm optimization (PSO) algorithm is widely used in the field of image retrieval, no one use it for directly feature weighting. Information extracted from user feedbacks will guide particles in order to find the optimal weights of various features of images (Color-, shape- or texture-based features). Fusion of these very non-homogenous features need a feature weighting algorithm that will take place by the help of PSO algorithm. Accordingly, an innovative fitness function is proposed to evaluate each particle’s position. Experimental results on Wang dataset and Corel-10k indicate that average precision of the proposed method is higher than other semi-automatic and automatic approaches. Moreover, the proposed method suggest a reduction in the computational complexity in comparison to other PSO-based image retrieval methods.


[1] K. Guo, R. Zhang, Z. Zhou, Y. Tang, and L. Kuang, "Combined Retrieval: A Convenient and Precise Approach for Internet Image Retrieval," Information Sciences, vol. 358-359, pp. 151-163, 2016/09/01/ 2016, doi:


[2] A. S. Hsu W1, Long LR, Neve L, Thoma GR., "SPIRS: a Web-Based Image Retrieval System for Large Biomedical Databases," International Journal of Biomedical Informatics, Vol. 78, pp. 13-24, 2009.


[3] C. Huang, H. Xu, L. Xie, J. Zhu, C. Xu, and Y. Tang, "Large-Scale Semantic Web Image Retrieval Using Bimodal Deep Learning Techniques," Information Sciences, Vol. 430-431, pp. 331-348, 2018/03/01/ 2018, doi:


[4] R. Lan, H. Wang, S. Zhong, Z. Liu, and X. Luo, "An Integrated Scattering Feature with Application to Medical Image Retrieval," Computers & Electrical Engineering, Vol. 69, pp. 669-675, 2018/07/01/ 2018, doi:

[5] Yogesh Kumar, Ashutosh Aggarwal, and S. Tiwari, "An Efficient and Robust Approach for Biomedical Image Retrieval using Zernike Moments," Biomedical Signal Processing and Control, Vol. 39, pp. 459-473, 2018.


[6] A. khodaskar and S. Ladhake, "New-Fangled Alignment of Ontologies for Content Based Semantic Image Retrieval," Procedia Computer Science, Vol. 48, pp. 298-303, 2015/01/01/ 2015, doi:


[7] M. K. Sharma and T. J. Siddiqui, "An Ontology Based Framework for Retrieval of Museum Artifacts," Procedia Computer Science, Vol. 84, pp. 169-176, 2016/01/01/ 2016, doi:


[8] V. K. G. P. Shamna, K.A. Abdul Nazeer,, "Content based Medical Image Retrieval using Topic and Location Model," Journal of Biomedical Informatics, Vol. 91, 2019,.


[9] S. Zhang, Q. Tian, G. Hua, Q. Huang, and W. Gao, "ObjectPatchNet: Towards scalable and semantic image annotation and retrieval," Computer Vision and Image Understanding, Vol. 118, pp. 16-29, 2014/01/01/ 2014, doi:


[10] R. W. Daiguo Deng, Hefeng Wu, Huayong He, Qi Li, Xiaonan Luo, "Learning Deep Similarity Models with Focus Ranking for Fabric Image Retrieval," Image and Vision computing, Vol. 70, pp. 11-20, 2018.


[11] M. M. Mohammed, A. Badr, and M. B. Abdelhalim, "Image Classification and Retrieval using Optimized Pulse-Coupled Neural Network," Expert Systems with Applications, Vol. 42, No. 11, pp. 4927-4936, 2015/07/01/ 2015, doi:


[12] M. Tzelepi and A. Tefas, "Deep convolutional learning for Content Based Image Retrieval," Neurocomputing, Vol. 275, pp. 2467-2478, 2018/01/31/ 2018, doi:


[13] A. Irtaza, A. Jaffar, E. Aleisa, and T.-S. Choi, "Embedding Neural Networks For Semantic Association in Content Based Image Retrieval," Multimedia Tools and Applications, Vol. 72, 09/01 2014, doi: 10.1007/s11042-013-1489-6.


[14] A. Sarwar, Z. Mehmood, T. Saba, K. A. Qazi, A. Adnan, and H. Jamal, "A Novel Method for Content-Based Image Retrieval to Improve the Effectiveness of the Bag-Of-Words Model Using a Support Sector Machine," Journal of Information Science, Vol. 45, No. 1, pp. 117-135, 2019/02/01 2018, doi: 10.1177/0165551518782825.


[15] S. Priyanka, "Microstructure Pattern Extraction Based Image Retrieval," Multimedia Tools and Applications, Vol. 79, No. 3, pp. 2263-2283, 2020/01/01 2020, doi: 10.1007/s11042-019-08113-y.


[16] M. Rao, B. Rao, and D. Govardhan, "CTDCIRS: Content based Image Retrieval System based on Dominant Color and Texture Features," International Journal of Computer Applications, Vol. 18, 03/31 2011, doi: 10.5120/2285-2961.


[17] R. S. M. Premkumar, "Interactive Content Based Image Retrieval using Multiuser Feedback," International Journal on Informatics Visualization, Vol. 1, No. 4, pp. 165 - 169, 2017.


[18] N. M. A. J. Li, "Relevance Feedback in Content-Based Image Retrieval: A Survey," in Handbook on Neural Information Processing, Vol. 49. Berlin, Heidelberg: Springer, 2013, pp. 433-469.


[19] M. M. Rahman, S. K. Antani, and G. R. Thoma, "A Learning-Based Similarity Fusion and Filtering Approach for Biomedical Image Retrieval Using SVM Classification and Relevance Feedback," IEEE Transactions on Information Technology in Biomedicine, Vol. 15, No. 4, pp. 640-646, 2011, doi: 10.1109/TITB.2011.2151258.


[20] A. Marakakis, G. Siolas, N. Galatsanos, A. Likas, and A. Stafylopatis, "Relevance Feedback Approach for Image Retrieval Combining Support Vector Machines and Adapted Gaussian Mixture Models," IET Image Processing, Vol. 5, No. 6, pp. 531-540, 2011, doi: 10.1049/iet-ipr.2009.0402.


[21]         M. Cai-Hong, D. Qin, and L. Shi-Bin, "A Hybrid PSO and Active Learning SVM Model for Relevance Feedback in the Content-Based Images Retrieval," in 2012 International Conference on Computer Science and Service System, 11-13 Aug. 2012 2012, pp. 130-133, doi: 10.1109/CSSS.2012.40.


[22] M. Broilo and F. G. B. D. Natale, "A Stochastic Approach to Image Retrieval Using Relevance Feedback and Particle Swarm Optimization," IEEE Transactions on Multimedia, Vol. 12, No. 4, pp. 267-277, 2010, doi: 10.1109/TMM.2010.2046269.


[23] F. Baig et al., "Boosting the Performance of the BoVW Model Using SURF–CoHOG-Based Sparse Features with Relevance Feedback for CBIR," Iranian Journal of Science and Technology, Transactions of Electrical Engineering, Vol. 44, No. 1, pp. 99-118, 2020/03/01 2020, doi: 10.1007/s40998-019-00237-z.


[24] A. Latif et al., "Query-Sensitive Similarity Measure for Content-based Image Retrieval Using Meta-Heuristic Algorithm," Journal of King Saud University - Computer and Information Sciences, Vol. 30, No. 3, pp. 373-381, 2018/07/01/ 2018, doi:


[25] A. Ameer and K. S. Kumar, "Efficient Automatic Image Annotation Using Optimized Weighted Complementary Feature Fusion Using Genetic Algorithm," Procedia Computer Science, Vol. 58, pp. 731-739, 2015/01/01/ 2015, doi:



[26] M. Arevalillo-Herráez, F. J. Ferri, and S. Moreno-Picot, "Improving Distance Based Image Retrieval Using Non-Dominated Sorting Genetic Algorithm," Pattern Recognition Letters, Vol. 53, pp. 109-117, 2015/02/01/ 2015, doi:


[27] Z. Shoaie and S. Jinni, "Semantic image retrieval using relevance feedback and reinforcement learning algorithm," in 2010 5th International Symposium On I/V Communications and Mobile Network, 30 Sept.-2 Oct. 2010 2010, pp. 1-4, doi: 10.1109/ISVC.2010.5654900.


[28] L. Pinjarkar, M. Sharma, and S. Selot, "Deep CNN Combined With Relevance Feedback for Trademark Image Retrieval," Journal of Intelligent Systems, 09/15 2018, doi: 10.1515/jisys-2018-0083.


[29] X. Heng, W. Jun-yi, and M. Lei, "Relevance Feedback for Content-based Image Retrieval using Deep Learning," in 2017 2nd International Conference on Image, Vision and Computing (ICIVC), 2-4 June 2017 2017, pp. 629-633, doi: 10.1109/ICIVC.2017.7984632.


[30] C. K. L. Banerjee, A. E. Devorah, B. Do, D. L. Rubin, C. F. Beaulieu, "Relevance Feedback for Enhancing Content based Image Retrieval and Automatic Prediction of Semantic Image Features: Application to Bone Tumor Radiographs," J Biomed Inform,Vol. 84, pp. 123-135, 2018.


[31] A. Sleit, A. Abu Dalhoum, M. Qatawneh, M. Al-Sharief, R. a. Al-Jabaly, and O. Karajeh, "Image Clustering using Color, Texture and Shape Features," KSII Transactions on Internet and Information Systems, Nol. 5, pp. 211-227, 01/21 2011, doi: 10.3837/tiis.2011.01.012.


[32] A. A. Fahimeh Alaei, Umapada Pal, Michael Blumenstein, "A Comparative Study of Different Texture Features For Document Image Retrieval," Expert Systems with Applications, Vol. 121, pp. 97-114, 2019.


[33] J. Z. Wang, L. Jia, and G. Wiederhold, "SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23,No. 9, pp. 947-963, 2001, doi: 10.1109/34.955109.


[34] A. R. Y. Betül Sultan Yıldız, "Comparison of Grey Wolf, Whale, Water Cycle, Ant Lion and Sine-Cosine Algorithms For The Optimization of a Vehicle Engine Connecting Rod," Materials Testing, Vol. 60, No. 3, pp. 311-315, 2018.


[35] A. Yildiz, "A New Hybrid Artificial Bee Colony Algorithm for Robust Optimal Design and Manufacturing," Applied Soft Computing, Vol. 13, pp. 2906–2912, 05/01 2013, doi: 10.1016/j.asoc.2012.04.013.


[36] A. Yildiz, "A Novel Hybrid Immune Algorithm for Global Optimization in Design and Manufacturing," Robotics and Computer-Integrated Manufacturing, Vol. 25, pp. 261-270, 04/01 2009, doi: 10.1016/j.rcim.2007.08.002.


[37] X. Meng and Z. Pian, "Theoretical Basis for Intelligent Coordinated Control," in Intelligent Coordinated Control of Complex Uncertain Systems for Power Distribution Network Reliability, X. Meng and Z. Pian Eds. Oxford: Elsevier, 2016, Ch. 2, pp. 15-50.


[38] A. Yildiz and B. Yıldız, "The Harris hawks optimization algorithm, salp swarm algorithm, grasshopper optimization algorithm and dragonfly algorithm for structural design optimization of vehicle components," Materialprufung, Vol. 8, pp. 60-70, 08/03 2019, doi: 10.3139/120.111379.


[39] A. Yildiz and K. Solanki, Multi-Objective Optimization Of Vehicle Crashworthiness Using Paticle Swarm Optimization Approach. 2012.

[40] Z. Wei, G. Liu, "Image Retrieval Using the Intensity Variation Descriptor," Mathematical Problems in Engineering, 2020.