Intensified Image Retrivel System :Non-Linear Mutation Based Genetic Algorithm
DOI:
https://doi.org/10.22399/ijcesen.723Keywords:
Image Retrieval System, Genetic Algorithm (GA), Visual Geometry Group, Reddit Website, Convolutional Neural NetworksAbstract
Globally, increasing amount of websites and data bases leads to utilization of enormous image data used in the system. The users interact with the images which are downloaded, stocked, uploaded and transmitting the data on regularly. Conversely, searching the particular images within the huge files and websites is the daunting problem. Traditionally, manual searching is not only time consuming but also less accurate process. Moreover, it requires the assistance of expertise for the image searching mechanism. To resolve the issue, an efficient IRS (Image Retrieval System) is needed for the users to automate the searching process. To address the issue, several conventional model attempted to achieve efficient IRS. However, existing researches limited in accuracy and speed. To overcome the limitations, proposed model utilized non-linear mutation updation based GA (Genetic Algorithm) for Reddit based IRS on eight classes. The GA is used for the capability of global optimization, parallelism and greater set of solution space. Nevertheless, it is sort by limited drawbacks such as unguided mutation, local extremum and convergence. To resolve the issue, non-linear mutation updation is added in the GA technique to improve the IRS efficiency. Moreover, VGG-16 algorithm is utilized for the feature extraction method to extract significant features from the data. Correspondingly, input data is acquired from the popular website called reddit which allows the users to upload and share images with the own post and comments of the users. The data is extracted through the API mechanism with the specific hashtag content. The performance of the proposed model is calculated using performance metrics in order to evaluate the IRS efficacy. Moreover, internal comparison is processed in the respective research to reveals the better efficiency of the proposed model.
References
Mohammed, M.A., Z.A. Oraibi, and M.A. Hussain. (2023). Content-Based Image Retrieval using Hard Voting Ensemble Method of Inception, Xception, and Mobilenet Architectures. Iraqi Journal for Electrical And Electronic Engineering. 19(2). DOI:10.37917/ijeee.19.2.17 DOI: https://doi.org/10.37917/ijeee.19.2.17
Liu, G.-H., et al. (2024). Exploiting sublimated deep features for image retrieval. Pattern Recognition. 147: 110076. DOI:10.1016/j.patcog.2023.110076 DOI: https://doi.org/10.1016/j.patcog.2023.110076
Naeem, A., et al. (2023). Deep learned vectors’ formation using auto-correlation, scaling, and derivations with CNN for complex and huge image retrieval. Complex & Intelligent Systems. 9(2): 1729-1751. DOI:10.1007/s40747-022-00866-8 DOI: https://doi.org/10.1007/s40747-022-00866-8
Yang, X., et al. (2019). BoSR: A CNN-based aurora image retrieval method. Neural Networks. 116: 188-197. DOI: 10.1016/j.neunet.2019.04.012 DOI: https://doi.org/10.1016/j.neunet.2019.04.012
Sezavar, A., H. Farsi, and S. Mohamadzadeh. (2019). A modified grasshopper optimization algorithm combined with cnn for content based image retrieval. International Journal of Engineering. 32(7): 924-930. DOI:10.5829/ije.2019.32.07a.04 DOI: https://doi.org/10.5829/ije.2019.32.07a.04
Cao, R., et al. (2020). Enhancing remote sensing image retrieval using a triplet deep metric learning network. International Journal of Remote Sensing. 41(2):740-751. DOI:10.1080/2150704X.2019.1647368 DOI: https://doi.org/10.1080/2150704X.2019.1647368
Wang, L., et al. (2019). Enhancing sketch-based image retrieval by cnn semantic re-ranking. IEEE transactions on cybernetics. 50(7):3330-3342. DOI: 10.1109/TCYB.2019.2894498 DOI: https://doi.org/10.1109/TCYB.2019.2894498
Husain, S.S. and M. Bober. (2019). EMAP: Multi-layer entropy-guided pooling of dense CNN features for image retrieval. IEEE Transactions on Image Processing. 28(10):5201-5213. DOI:10.1109/TIP.2019.2917234 DOI: https://doi.org/10.1109/TIP.2019.2917234
Sundararajan, S.K., B. Sankaragomathi, and D.S. Priya. (2019). Deep belief CNN feature representation based content based image retrieval for medical images. Journal of medical systems. 43: 1-9. DOI:10.1007/s10916-019-1305-6 DOI: https://doi.org/10.1007/s10916-019-1305-6
Gkelios, S., et al. (2021). Deep convolutional features for image retrieval. Expert Systems with Applications. 177: 114940. DOI:10.1016/j.eswa.2021.114940 DOI: https://doi.org/10.1016/j.eswa.2021.114940
Putzu, L., L. Piras, and G. Giacinto. (2020). Convolutional neural networks for relevance feedback in content based image retrieval: A Content based image retrieval system that exploits convolutional neural networks both for feature extraction and for relevance feedback. Multimedia Tools and Applications. 79: 26995-27021. DOI:10.1007/s11042-020-09292-9 DOI: https://doi.org/10.1007/s11042-020-09292-9
Hoxha, G., F. Melgani, and B. Demir. (2020). Toward remote sensing image retrieval under a deep image captioning perspective. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13: 4462-4475. DOI:10.1109/JSTARS.2020.3013818 DOI: https://doi.org/10.1109/JSTARS.2020.3013818
Xu, S., W. Chou, and H. Dong. (2019). A robust indoor localization system integrating visual localization aided by CNN-based image retrieval with Monte Carlo localization. Sensors. 19(2): p. 249. DOI:10.3390/s19020249 DOI: https://doi.org/10.3390/s19020249
Saritha, R.R., V. Paul, and P.G. Kumar. (2019). Content based image retrieval using deep learning process. Cluster Computing. 22(1): p. 4187-4200. DOI:10.1007/s10586-018-1731-0 DOI: https://doi.org/10.1007/s10586-018-1731-0
Wang, H., et al. (2020). Visual saliency guided complex image retrieval. Pattern Recognition Letters,. 130: p. 64-72. DOI:10.1016/j.patrec.2018.08.010 DOI: https://doi.org/10.1016/j.patrec.2018.08.010
Lu, H., et al. (2020). Deep fuzzy hashing network for efficient image retrieval. IEEE transactions on fuzzy systems. 29(1):166-176. DOI:10.1109/TFUZZ.2020.2984991 DOI: https://doi.org/10.1109/TFUZZ.2020.2984991
Shao, Z., et al. (2020). Multilabel remote sensing image retrieval based on fully convolutional network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13(1): 318-328. DOI:10.1109/JSTARS.2019.2961634 DOI: https://doi.org/10.1109/JSTARS.2019.2961634
Chhabra, P., N.K. Garg, and M. Kumar. (2020). Content-based image retrieval system using ORB and SIFT features. Neural Computing and Applications. 32(2): 2725-2733. DOI:10.1007/s00521-018-3677-9 DOI: https://doi.org/10.1007/s00521-018-3677-9
BOTSALI, A. R., & ALAYKIRAN , K. (2020). Analysis of TSP: Simulated Annealing and Genetic Algorithm Approaches. International Journal of Computational and Experimental Science and Engineering, 6(1), 23–28. Retrieved from https://ijcesen.com/index.php/ijcesen/article/view/111 DOI: https://doi.org/10.22399/ijcesen.637445
DEMİR, H. I., ERDEN, C., DEMİRİZ, A., DUGENCİ, M., & UYGUN, O. (2017). Integrating Process Planning, WATC Weighted Scheduling, and WPPW Weighted Due-Date Assignment Using Pure and Hybrid Metaheuristics for Weighted Jobs. International Journal of Computational and Experimental Science and Engineering, 3(1), 11–20. Retrieved from https://ijcesen.com/index.php/ijcesen/article/view/33 DOI: https://doi.org/10.22399/ijcesen.323860
S, D., C, venkatesh, M, M., S.Archana Devi, & T, J. (2024). Optimal Speed Control of Hybrid Stepper Motors through Integrating PID Tuning with LFD-NM Algorithm. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.489 DOI: https://doi.org/10.22399/ijcesen.489
Nagalapuram, J., & S. Samundeeswari. (2024). Genetic-Based Neural Network for Enhanced Soil Texture Analysis: Integrating Soil Sensor Data for Optimized Agricultural Management. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.572 DOI: https://doi.org/10.22399/ijcesen.572
J Jeysudha, K. Deiwakumari, C.A. Arun, R. Pushpavalli, Ponmurugan Panneer Selvam, & S.D. Govardhan. (2024). Hybrid Computational Intelligence Models for Robust Pattern Recognition and Data Analysis . International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.624 DOI: https://doi.org/10.22399/ijcesen.624
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