large scale partially duplicated web image retrieval
The state-of-the-art image retrieval approaches represent images with a high dimensional vector of visual words by quantizing local features, such as SIFT, in the descriptor space. The geometric clues among visual words in an image is usually ignored or exploited for full geometric verification, which is computationally expensive. In recent years, partially duplicated images are prevalent on the web. In this demo, we focus on partial-duplicated web image retrieval, and propose a retrieval system based on a novel scheme, spatial coding, to encode the spatial information among local features in an image. Our spatial coding is both efficient and effective to discover false matches of local features between images, and can greatly improve retrieval performance.
large scale image retrieval partial-duplicate