Project abstract for group papaniko

Fast Coding for Approximate Nearest Neighbors on High-Dimensional Image Data

Nearest Neighbors (NN) is a fundamental problem in computer science and is used as a core algorithmic component in computer vision applications such as image search, visual surveillance, image registration, etc. Inspired by recent advances in signal processing and compressive sensing, these researchers aim to sparse code each data point using a learned basis dictionary. Indices of the dictionary’s support sets are used to generate one compact identifier for each data point. This generates a small code for each data point, which can be stored and retrieved fast using a hash table mechanism. Typically, most real world datasets worked with in computer vision consist of billions of high-dimensional data points, and demand large computational and storage resources for the learning and coding phases.