Knee osteoarthritis (KOA) is a degenerative joint condition affecting many people across the globe. The KOA offers a serious healthcare concern owing to the steady degeneration of knee joints. This condition causes pain and inhibits the movement of a person. Early detection of KOA is crucial for implementing successful treatment strategies and preventing disease progression. Advancements in Computer-Aided Detection (CAD) and Computer Vision (CV) technologies have paved the way for more effective and accurate diagnosis of KOA. This study explores the effectiveness of the Local Directional Octa Pattern (LDOP), a computationally minimal yet effective approach, in reliably identifying early-stage KOA. LDOP gathers essential information from images, such as joint space narrowing and osteophyte development, to differentiate among healthy and KOA-affected knees. This LDOP based KOA detection system produced the best accuracy (97.8%) when used with the top 20 most similar images. In this study, LDOP outperformed deep learning-based approaches for KOA detection. This research showcases the capabilities of LDOP of being a reliable tool for accurate and timely detection of KOA in clinical settings, paving a pathway for better patient outcomes.