Rate-efficient visual correspondences using random projections

We consider the problem of establishing visual correspondences in a distributed and rate-efficient fashion by broadcasting compact descriptors. Establishing visual correspondences is a critical task before other vision tasks can be performed in a wireless camera network. We propose the use of coarsely quantized random projections of descriptors to build binary hashes, and use the Hamming distance between binary hashes as the matching criterion. In this work, we derive the analytic relationship of Hamming distance between the binary hashes to Euclidean distance between the original descriptors. We present experimental verification of our result, and show that for the task of finding visual correspondences, sending binary hashes is more rate-efficient than prior approaches.