Convolutional Hashing for Automated Scene Matching

Many information retrieval tasks rely on high dimensional searches, including K-nearest neighbors (KNN), approximate nearest neighbors (ANN), and exact r-neighbor lookup in Hamming space. At scale, these searches are enabled by indexes on binary hashes, such as locality-sensitive hashing (LSH) and multi-indexing [1]. Recent research has flourished on these topics due to enormous growth in data volume and industry applications [2]. We present a powerful new approach to a fundamental challenge in these tasks: learning a good binary hash function. We demonstrate the effectiveness of our method by applying it to the task of automated scene matching (ASM) with a multi-index system. We call our model convolutional hashing for automated scene matching (CHASM). To the best of our knowledge, it is the first neural network to outperform state-of-the-art hash functions like Haar wavelets and color layout descriptors at ASM across the board.