Representative local features mining for large-scale near-duplicates retrieval

Local features have been widely used in many computer vision related researches, such as near-duplicate image and video retrieval. However, the storage and query cost of local features become prohibitive on large-scale database. In this paper, we propose a representative local features mining method to generate a compact but more effective feature subset. First, we do an unsupervised annotation for all similar images(or frames in video) in the database. Second, we compute a comprehensive score for every local feature. The score function combines the robustness and discrimination. Finally, we sort all the local features in an image by their scores and the low-score local features can be removed. The selected local features are robust and discriminative, which can guarantee the better retrieval quality than using full of the original feature set. By our method, the number of local features can be significantly reduced and a large amount of storage and computational cost can be saved. The experimental results show that we can use 30% of the features to get a better query performance than that of full feature set.