The scalable vocabulary tree based model for sub-image retrieval

摘要

The paper gives a performance research of re-ranking in sub-image retrieval using scalable vocabulary tree (SVT) which is built from local Speed Up Robust Features (SURF) descriptors. Firstly, the paper gives a study on retrieval performance using different single layers of the tree, which tells it divides data too coarsely for low layers with a small quantity of leaf nodes, while too finely for the 6-th layer with too many leaf nodes. Then using the best selected layer, the authors give a comparative analysis with popular advanced re-ranking strategies in the existing literatures. Finally, the authors propose a weighted score method that calculates matching score from dominating layers. The experimental results prove that the weighted score method achieves almost optimal retrieval performance when using SVT for data representations. Meanwhile, it almost doesn't increase any computational complexity, and can be implemented easily.