Scalable Image Search with Multiple Index Tables

Abstract

Motivated by scalable partial-duplicate visual search, there has been growing interest in a wealth of compact and efficient binary feature descriptors (e.g. ORB, FREAK, BRISK). Typically, binary descriptors are clustered into codewords and quantized with Hamming distance, following the conventional bag-of-words strategy. However, such codewords formulated in Hamming space do not present obvious indexing and search performance improvement as compared to the Euclidean codewords. In this paper, without explicit codeword construction, we explore the use of partial binary descriptors as direct codebook indices (addresses). We propose a novel approach to build multiple index tables which concurrently check for collision of the same hash values. The evaluation is performed on two public image datasets: DupImage and Holidays. The experimental results demonstrate the indexing efficiency and retrieval accuracy of our approach.