Visual query compression with locality preserving projection on Grassmann manifold
For a variety of visual search and visual key points based navigation applications, compression of visual key point features like SIFT is an important part of the overall system that can directly affect the efficiency and latency. In this work, we examine a new approach in visual key points compression, that utilizes subspaces that optimized for preserving key point feature matching properties than the reconstruction performance, and allows for a set of optimal subspaces on Grassmann manifold that can better adapt to the local manifold geometry. The simulation demonstrates that such scheme has very low overhead in signaling subspaces, and has very much improved performance on the repeatability of the keypoint matching subject to bit rate constraints.