Feature Repetitiveness Similarity Metrics in Visual Search

Repetitive patterns are significant visual cues for matching and detecting objects in images. However, information from repetitive patterns is underutilized in computer vision algorithms as they cause burstiness issue in image similarity scoring. Existing similarity metrics do not take the repetitive patterns into account, and hence cannot handle images with repetitive patterns well. In view of this, this letter presents a new feature repetitiveness similarity (FRS) metric that not only addresses the burstiness issue, but also uses the information from the repetitive patterns to enhance the retrieval performance. The proposed FRS framework detects the repetitive patterns using descriptor and geometric information of local features in the images. Unique and repetitive features are handled separately and then fused at scoring stage using the FRS metric. Experiments conducted on the benchmark Oxford and Paris datasets show that the proposed method outperforms the state-of-the-art methods by a mean average precision of 6%. This demonstrates the effectiveness of FRS metric in matching and retrieval of images with repeated patterns.