Composite Quantization

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 41; no. 6; pp. 1308 - 1322
• Main Authors: Wang, Jingdong, Zhang, Ting
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Approximate nearest neighbor search; composite quantization; Dictionaries; Encoding; Measurement; near-orthogonality; Nearest neighbor methods; Orthogonality; Quantization; Searching; Upper bounds
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2018.2835468

This paper studies the compact coding approach to approximate nearest neighbor search. We introduce a composite quantization framework. It uses the composition of several (M) elements, each of which is selected from a different dictionary, to accurately approximate a D-dimensional vector, thus yielding accurate search, and represents the data vector by a short code composed of the indices of the selected elements in the corresponding dictionaries. Our key contribution lies in introducing a near-orthogonality constraint, which makes the search efficiency is guaranteed as the cost of the distance computation is reduced to O(M) from O(D) through a distance table lookup scheme. The resulting approach is called near-orthogonal composite quantization. We theoretically justify the equivalence between near-orthogonal composite quantization and minimizing an upper bound of a function formed by jointly considering the quantization error and the search cost according to a generalized triangle inequality. We empirically show the efficacy of the proposed approach over several benchmark datasets. In addition, we demonstrate the superior performances in other three applications: combination with inverted multi-index, inner-product similarity search, and query compression for mobile search.