Saliency Map-based Image Retrieval using Invariant Krawtchouk Moments

TL;DR

This paper presents a saliency map-based image retrieval method using invariant Krawtchouk moments that improves accuracy and speed by combining multiple feature types and saliency detection, validated on standard datasets.

Contribution

Introduces a novel image retrieval approach combining saliency maps, invariant Krawtchouk moments, LBPs, and color histograms within a BoVW framework for enhanced performance.

Findings

Outperforms recent state-of-the-art methods on Caltech 101 and Wang datasets.

Achieves higher retrieval accuracy and efficiency.

Effectively isolates foreground features for better discrimination.

Abstract

With the widespread adoption of digital devices equipped with cameras and the rapid development of Internet technology, numerous content-based image retrieval systems and novel image feature extraction techniques have emerged in recent years. This paper introduces a saliency map-based image retrieval approach using invariant Krawtchouk moments (SM-IKM) to enhance retrieval speed and accuracy. The proposed method applies a global contrast-based salient region detection algorithm to create a saliency map that effectively isolates the foreground from the background. It then combines multiple orders of invariant Krawtchouk moments (IKM) with local binary patterns (LBPs) and color histograms to comprehensively represent the foreground and background. Additionally, it incorporates LBPs derived from the saliency map to improve discriminative power, facilitating more precise image differentiation. A bag-of-visual-words (BoVW) model is employed to generate a codebook for classification and discrimination. By using compact IKMs in the BoVW framework and integrating a range of region-based feature-including color histograms, LBPs, and saliency map-enhanced LBPs, our proposed SM-IKM achieves efficient and accurate image retrieval. Extensive experiments on publicly available datasets, such as Caltech 101 and Wang, demonstrate that SM-IKM outperforms recent state-of-the-art retrieval methods. The source code for SM-IKM is available at github.com/arnejad/SMIKM.