Content Based Image Retrieval Using Exact Legendre Moments and Support Vector Machine

TL;DR

This paper proposes a CBIR system using Exact Legendre Moments for shape feature extraction, combined with SVM classification, demonstrating improved efficiency and accuracy over existing methods.

Contribution

The work introduces an orthogonal, computationally efficient CBIR system based on Exact Legendre Moments and enhances classification with SVM, outperforming previous shape-based methods.

Findings

Superior retrieval efficiency and speed compared to MI and ZM methods.

Enhanced classification accuracy using SVM classifier.

Outperforms existing algorithms like SERVE with MMI.

Abstract

Content Based Image Retrieval (CBIR) systems based on shape using invariant image moments, viz., Moment Invariants (MI) and Zernike Moments (ZM) are available in the literature. MI and ZM are good at representing the shape features of an image. However, non-orthogonality of MI and poor reconstruction of ZM restrict their application in CBIR. Therefore, an efficient and orthogonal moment based CBIR system is needed. Legendre Moments (LM) are orthogonal, computationally faster, and can represent image shape features compactly. CBIR system using Exact Legendre Moments (ELM) for gray scale images is proposed in this work. Superiority of the proposed CBIR system is observed over other moment based methods, viz., MI and ZM in terms of retrieval efficiency and retrieval time. Further, the classification efficiency is improved by employing Support Vector Machine (SVM) classifier. Improved retrieval results are obtained over existing CBIR algorithm based on Stacked Euler Vector (SERVE) combined with Modified Moment Invariants (MMI).