## 1 Introduction
In principle, adjusting the coupling levels of these two key components can  
Figure 1: Repeated patterns vary in terms of content diversity and topology regularity. result in methods that are able to detect repeated patterns from any images, namely images with various combinations of the two complexity dimensions defined in Figure 1, e.g., with similar content but varying topological structures or with similar topology structure but varying contents. This study proposes a _general-purpose hybrid method_ that combines the strengths of deep feature extraction with an autocorrelation-based optimization model, overcoming the limitations of existing methods. The method is designed to detect repeated patterns robustly and efficiently across a wide range of image types, including those with diverse content and structural complexity. Specifically, the proposed method leverages pre-trained CNNs for efficient feature extraction, avoiding the need to train a dedicated model and ensuring scalability across various image types. The method also introduces an autocorrelation-based optimization algorithm to dynamically refine the detection of pattern sizes and positions, adapting to irregular or noisy data. Additionally, the method incorporates a feature selection mechanism based on edge detection to extract a small, informative subset of feature maps, significantly reducing computational cost without compromising accuracy. In order to demonstrate the practical value of the proposed method, the method was applied to detect repeated patterns from textile images. To this end, a dataset of 841 textile images with manually annotated ground truth was developed in this study that provides a reliable benchmark for repeated pattern detection research. This dataset reflects the inherent complexity of textile patterns, characterized by diverse visual contents and near-regular structures, making it an ideal testbed for validating the proposed method's performance. The main contributions are summarized as follows:  
* It introduces the first hybrid method that combines deep feature extraction with an autocorrelation-based optimization model, achieving a unique balance of robustness, speed, and adaptability. * A feature selection mechanism based on edge detection is introduced, which significantly reduces computational cost while maintaining accuracy. * An autocorrelation model with a dynamic similarity threshold is designed to efficiently refine repeated pattern sizes, adapting to variations in image complexity and irregularity. * A dataset of 841 textile images with manually annotated ground truth is developed, providing a reliable benchmark for repeated pattern detection research. * Extensive experiments demonstrate the superior accuracy and efficiency of the proposed method compared to state-of-the-art (SOTA) approaches, with ablation studies validating the effectiveness of individual components. Although textile images, mostly with near fronto-parallel repeated patterns of high resolutions and varying contents, serve as an excellent representative testbed, the proposed method is not limited to these type of images only. The proposed method can adapt to other types of repeated patterns with irregular structures by adjusting the coupling between the relevant modules designed for (1) feature extraction and processing and (2) prediction model optimization.