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The growing amount of data available in modern-day datasets makes the need toefficiently search and retrieve information. To make large-scale searchfeasible, Distance Estimation and Subset Indexing are the main approaches.Although binary coding has been popular for implementing both techniques, n-arycoding (known as Product Quantization) is also very effective for DistanceEstimation. However, their relative performance has not been studied for SubsetIndexing. We investigate whether binary or n-ary coding works better underdifferent retrieval strategies. This leads to the design of a new n-ary codingmethod, "Linear Subspace Quantization (LSQ)" which, unlike other n-aryencoders, can be used as a similarity-preserving embedding. Experiments onimage retrieval show that when Distance Estimation is used, n-ary LSQoutperforms other methods. However, when Subset Indexing is applied,interestingly, binary codings are more effective and binary LSQ achieves thebest accuracy.

On Large-Scale Retrieval: Binary or n-ary Coding?