A Distributed Dynamic Parallel Algorithm for SIFT Feature Extraction

This paper deals with the issue of developing efficient algorithms for accelerating SIFT (Scale Invariant Feature Transform) features extraction under distributed environment. The proposed distributed dynamic parallel algorithm (DDP-SIFT) using a special data parallel approach that divides the Gauss Scale Space by octave aimed at acquiring large image blocks which is of great importance in some application. To make this approach effective, steps of building Gauss Scale Space are changed, and only the prerequisite part which is only 1/13 of the whole pyramid will be produced before tasks allocation, and Allocation Data Quantity (ADQ) is decreased by 13 times. Data blocks are assembled as tasks maintained in task lists, and dynamically allocated to Computing Nodes. A refined-blocking approach is proposed to further improve load balance. Our investigations show that the proposed algorithm has remarkable performance on accelerating SIFT features extraction while pursuing large data blocks.