Intensified Image Retrivel System :Non-Linear Mutation Based Genetic Algorithm
S. Senthilvel, G. Thailambal · International Journal of Computational and Experimental Science and Engineering · 2025
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.