The core of a content-based image retrieval (CBIR) system is based on an effective understanding of the visual contents of images due to which a CBIR system can be termed as accurate. One of the most prominent issues which affect the performance of a CBIR system is the semantic gap. It is a variance that exists between low-level patterns of an image and high-level abstractions as perceived by humans. A robust image visual representation and relevance feedback (RF) can bridge this gap by extracting distinctive local and global features from the image and by incorporating valuable information stored as feedback. To handle this issue, this article presents a novel adaptive complementary visual word integration method for a robust representation of the salient objects of the image using local and global features based on the bag-of-visual-words (BoVW) model. To analyze the performance of the proposed method, three integration methods based on the BoVW model are proposed in this article: (a) integration of complementary features before clustering (called as non-adaptive complementary feature integration), (b) integration of non-adaptive complementary features after clustering (called as a non-adaptive complementary visual words integration), and (c) integration of adaptive complementary feature weighting after clustering based on self-paced learning (called as a proposed method based on adaptive complementary visual words integration). The performance of the proposed method is further enhanced by incorporating a log-based RF (LRF) method in the proposed model. The qualitative and quantitative analysis of the proposed method is carried on four image datasets, which show that the proposed adaptive complementary visual words integration method outperforms as compared with the non-adaptive complementary feature integration, non-adaptive complementary visual words integration, and state-of-the-art CBIR methods in terms of performance evaluation metrics.

中文翻译：

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基于自适应局部和全局视觉单词建模以及基于日志的相关性反馈的BoVW模型，用于图像的语义检索

基于内容的图像检索（CBIR）系统的核心是基于对图像视觉内容的有效理解，因此，可以将CBIR系统称为准确的。影响CBIR系统性能的最突出问题之一是语义鸿沟。它是图像的低级模式与人类所感知的高级抽象之间的差异。强大的图像视觉表示和相关性反馈（RF）可以通过从图像中提取独特的局部和全局特征以及合并作为反馈存储的有价值信息来弥合这一差距。为了解决这个问题，本文提出了一种新颖的自适应互补视觉单词集成方法，该方法基于视觉单词袋（BoVW）模型使用局部和全局特征来稳健表示图像的显着对象。为了分析该方法的性能，本文提出了三种基于BoVW模型的集成方法：（a）聚类之前互补特征的集成（称为非自适应互补特征集成），（b）非聚类后​​的自适应互补特征（称为非自适应互补视觉单词集成），以及（c）基于自定步学习的聚类后的自适应互补特征加权集成（称为基于自适应互补视觉单词集成的建议方法） 。通过在提出的模型中合并基于对数的RF（LRF）方法，可以进一步提高提出的方法的性能。在四个图像数据集上对该方法进行了定性和定量分析，结果表明，与非自适应互补特征集成，非自适应互补视觉单词集成和状态估计相比，该自适应互补视觉单词集成方法的性能要好。性能评估指标方面最先进的CBIR方法。