Camera-based document image retrieval system using local features - comparing SRIF with LLAH, SIFT, SURF and ORB

In this paper, we present camera-based document retrieval systems using various local features as well as various indexing methods. We employ our recently developed features, named Scale and Rotation Invariant Features (SRIF), which are computed based on geometrical constraints between pairs of nearest points around a keypoint. We compare SRIF with state-of-the-art local features. The experimental results show that SRIF outperforms the state-of-the-art in terms of retrieval time with 90.8% retrieval accuracy.