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September 10, 2025Open Access

A multi-class SVM based CBIR system using Forest Optimization algorithm and Firefly algorithm

Key Points

The proposed method enhances classification accuracy in content based image retrieval systems using advanced algorithms.
Using multi-class support vector machine, features are classified into multiple categories for better retrieval outcomes.
Feature extraction includes texture and shape analysis along with a new approach for optimizing selection.
Evaluations conducted on multiple datasets demonstrate enhanced performance compared to existing image retrieval techniques.

Abstract

Abstract Content based image retrieval (CBIR) system extracts the images that are similar to the query image (QI) from database. In this work, a new CBIR system is introduced using feature extraction techniques, Forest Optimization Algorithm, Multi-Class Support Vector Machine (MC-SVM) and Firefly Algorithm. In this method, HSV colour model for colour feature extraction, CS-SCHT for texture feature extraction and Modified Exponent Fourier Moments for shape feature extraction are used for extracting the features which are related to query and images in the database. Forest Optimization Algorithm (FOA) is used for feature subset selection. Later, MC-SVM is used to classify the selected features into different classes. Lastly, the classification accuracy improved by using firefly algorithm (FA). The proposed method performance is evaluated on datasets Corel-1k, Corel-5k, Corel-10k, GHIM-10k, and CALTECH 101 databases and the results emphasise the proposed model has shown better results compared to the existing works.

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