The printed fabric images usually contain a large number of minimal repeated patterns (MRPs), which are of great value for the color separation, plate-making and other related applications in the textile industry. The key-points localization is a well established and effective manual method to detect the MRPs by finding a number of identical key-points. Recently, computer-aided automated key-points localization techniques have shown great potential and attracted considerable interest. Nevertheless, most of the available subtle and ad-hoc strategies are poorly generalized to complex situations due to a lack of systematic formulation. In this study, we endeavor to automate and theorize this manual detection method, aiming to enhance its accuracy, robustness, and efficiency. Specifically, we delineate related concepts and systematically formalize the minimal repeated pattern (MRP) detection problem, thereby establishing a theoretical groundwork for the manual method’s viability. Then, we present an effective and robust adjacent key-points localization (AKL) framework by automating manual detection method of MRP in printed fabric images. This framework is further developed into a comprehensive automated image retrieval system, complete with detailed implementation techniques. We also create a printed fabric dataset named PFI-10K containing approximately 10,000 images to test corresponding methods. The experimental results demonstrate the effectiveness and robustness of the proposed AKL framework.