Robust Spatial Matching for Object Retrieval and Its Parallel Implementation on GPU

Abstract

Spatial matching for object retrieval is often time-consuming and susceptible to viewpoint changes. To address this problem, we propose a novel spatial matching method that is robust to viewpoint changes and implement it on modern graphics processing unit (GPU) in parallel for real-time applications. Unlike previous spatial matching methods used in object retrieval, in which the affine transformation estimation is based on the gravity vector assumption, our method abandons this strong assumption by matching the affine covariant neighbors (ACNs) of corresponding local regions and estimating affine transformation from each single pair of corresponding local regions. Taking into account real-time applications, we implement the method on modern GPU in parallel to speed up the process. Computations are distributed evenly to threads with load balancing, and device memory accesses are optimized with bitmap-based parallel scan. Experimental results demonstrate that our method is more robust and more efficient than previous methods especially when the viewpoints are changed, and the parallel implementation on GPU obtains ten times speedup.