Scalable Image Search with Multiple Index Tables

Abstract

Motivated by scalable partial-duplicate visual search, there has been growing interest on 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, which follows conventional bag-of-words strategy. However, such codewords formulated in Hamming space did not present obvious indexing and search performance improvement as compared to the Euclidean ones. In this paper, without explicit codeword construction, we explore to utilize binary descriptors as direct codebook indices (addresses). We propose a novel approach to build multiple index tables which parallelly check the collision of same hash values. The evaluation is performed on two public image datasets: DupImage and Holidays. The experimental results demonstrate the index efficiency and retrieval accuracy of our approach.