Improved Keypoint Matching Method for Near-Duplicate Keyframe Retrieval

We propose a Near-Duplicate Keyframe (NDK) retrieval method that can handle extreme zooming and significant object motion. The first stage consists of eliminating false keypoint matches using symmetric property and a ratio of nearest and second-nearest neighbor distances. Then, a pattern coherency score is assigned to each pair of keyframes. These two features are combined through linear discriminant analysis (LDA) and the separating boundary is trained using SVM. Experiments are carried out for NDK retrieval on the Columbia and NTU datasets. The promising results confirm the effectiveness of our keypoint matching algorithm and show distinguishing power of our proposed features and feature weighting role in NDK retrieval.