Content Based-Image Retrieval Using Support Vector Machine

• Published in: Proceedings - 2021 11th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2021
• Main Author: Shahrom S.I.; Mohamed N.; Kamarudin S.F.; Ghazali W.N.A.W.; Malek A.A.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116224820&doi=10.1109%2fICCSCE52189.2021.9530873&partnerID=40&md5=ef4d25c26ffcc8eb7621cedf14d34dc2

Image retrieval is an important problem in multimedia systems. It is determined as the process of searching and fetching images from a dataset. Content-based Image Retrieval (CBIR) is a significant and challenging field of research in digital image processing. The CBIR system's essential requirement is to retrieve the relevant information following a query image with higher system output from a large image database. Unfortunately, not all the methods are suitable to be used to get high accuracy of retrieval. Therefore, this research aims to classify the data of query image with the data of image database to get a similar image retrieval using Support Vector Machine (SVM) and validate its accuracy based on the classification for the performance evaluation using the precision-recall measure. The critical point of SVM is to get an optimal hyperplane that separates the data points into two classes. This method was applied to different image databases because the classified-based proposed scheme proved better performance than various existing methods. This project assessed experimental results toward 500 images of the Caltech-256 image dataset to demonstrate the proposed method. For retrieving a similar image following the query image, 20 images from five classes were successfully retrieved by using this method. It will show that the SVM method's average accuracy from all image classes is 94.79%. © 2021 IEEE.