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. xtensive 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.

翻译：随着配备摄像头的数字设备广泛普及以及互联网技术的快速发展，近年来涌现了大量基于内容的图像检索系统与新颖的图像特征提取技术。本文提出一种基于显著性图并利用不变Krawtchouk矩的图像检索方法（SM-IKM），以提升检索速度与准确度。该方法采用基于全局对比度的显著区域检测算法生成显著性图，有效分离前景与背景；随后结合多阶不变Krawtchouk矩（IKM）、局部二值模式（LBP）与颜色直方图，对前景和背景进行综合表征。此外，通过引入从显著性图导出的LBP特征以增强判别能力，实现更精确的图像区分。研究采用视觉词袋（BoVW）模型生成用于分类与判别的码本。通过在BoVW框架中使用紧凑的IKM，并整合包括颜色直方图、LBP及显著性图增强LBP在内的多种区域特征，本文提出的SM-IKM方法实现了高效准确的图像检索。在Caltech 101、Wang等公开数据集上的大量实验表明，SM-IKM的性能优于当前先进的检索方法。SM-IKM的源代码公开于github.com/arnejad/SMIKM。