A Distributed Parallel Algorithm for SIFT Feature Extraction

SIFT(scale invariant feature transform) has been widely applied to object detection and recognition, image registration and fusion, texture recognition, scene classification, human face detection, image retrieval, 3D reconstruction, digital watermarking, and object tracking. However, it is compute-intensive and time-consuming. A distributed parallel algorithm for extracting SIFT features (DP-SIFT algorithm) is proposed using data parallel strategy on PC clusters/COW (cluster of workstation) based on message passing. An algorithm for data blocking with limitation on height and width is designed according to the specific characteristic of feature extraction space. Data distribution and feature adjustment methods are also presented. A strategy of data blocking coordinate with data passing approaches for communication optimization in image parallel processing is proposed after the effect of data blocking methods and data passing approaches on communication time are investigated. Experimental results verify that the DP-SIFT algorithm has remarkable performance on speedup and efficiency. On clusters of PCs with 32 cores linked by gigabit Ethernet, the speedup and efficiency can reach as high as 20 and 0.6 respectively when input image scale is 1024×768, and 18 and 0.56 when input image scale is 2048×1536.