IEEE Trans Pattern Anal Mach Intell - Optimized Product Quantization.

Tópicos

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Resumo

Product quantization (PQ) is an effective vector quantization method. A product quantizer can generate an exponentially large codebook at very low memory/time cost. The essence of PQ is to decompose the high-dimensional vector space into the Cartesian product of subspaces and then quantize these subspaces separately. The optimal space decomposition is important for the PQ performance, but still remains an unaddressed issue. In this paper, we optimize PQ by minimizing quantization distortions \wrt the space decomposition and the quantization codebooks. We present two novel solutions to this challenging optimization problem. The first solution iteratively solves two simpler sub-problems. The second solution is based on a Gaussian assumption and provides theoretical analysis of the optimality. We evaluate our optimized product quantizers in three applications: (i) compact encoding for exhaustive ranking \cite{Jegou2011a}, (ii) building inverted multi-indexing for non-exhaustive search \cite{Babenko2012}, and (iii) compacting image representations for image retrieval \cite{Jegou2010a}. In all applications our optimized product quantizers outperform existing solutions.

Resumo Limpo

product quantiz pq effect vector quantiz method product quantiz can generat exponenti larg codebook low memorytim cost essenc pq decompos highdimension vector space cartesian product subspac quantiz subspac separ optim space decomposit import pq perform still remain unaddress issu paper optim pq minim quantiz distort wrt space decomposit quantiz codebook present two novel solut challeng optim problem first solut iter solv two simpler subproblem second solut base gaussian assumpt provid theoret analysi optim evalu optim product quantiz three applic compact encod exhaust rank citejegoua ii build invert multiindex nonexhaust search citebabenko iii compact imag represent imag retriev citejegoua applic optim product quantiz outperform exist solut

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