Fast Democratic Aggregation and Query Fusion for Image Search

In image search using local features, to avoid indexing each feature individually, encoding methods are popularly adopted to embed and aggregate local features of an image into a compact vector. Democratic aggregation with triangulation embedding (T-embedding) exhibits significant retrieval accuracy improvement over previous works. However, it suffers high computational complexity. To address this problem and consistently improve the retrieval performance, we propose a new democratic method to accelerate aggregating step without accuracy lost. We also embed weak spatial context in the kernel construction to depress co-occurrence caused by local feature detector. Furthermore, we enhance the retrieval performance with an efficient query fusion strategy. The evaluation on public datasets shows that our democratic aggregation is an order of magnitude faster than the original democratic aggregation with comparable retrieval accuracy, and the query fusion achieves a significant accuracy improvement over previous works.