Abstract
It is prevalent to perform hashing on the basis of the well-known Principal Component Analysis (PCA), e.g., [1–4]. Of all those PCA-based methods, Iterative Quantization (ITQ) [1] is probably the most popular one due to its superior performance in terms of retrieval accuracy. However, the optimization problem in ITQ is severely under-deterministic, thereby the quality of the produced hash codes may be depressed. In this paper, we propose a new hashing method, termed Isotropic Iterative Quantization (IITQ), that extends the formulation of ITQ by incorporating properly the isotropic prior proposed by [3]. The optimization problem in IITQ is complicate, non-convex in nature and therefore not easy to solve. We devise a proximal method that can solve problem in a practical fashion. Extensive experiments on two benchmark datasets, CIFAR-10 [5] and 22K-LabelMe [6], show the superiorities of our IITQ over several existing methods.
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This work is supported by NSFC 61502238, NSFC 61532009, BK2012045 and 15KJA520001.
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Li, L., Liu, G., Liu, Q. (2016). Advancing Iterative Quantization Hashing Using Isotropic Prior. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9517. Springer, Cham. https://doi.org/10.1007/978-3-319-27674-8_16
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DOI: https://doi.org/10.1007/978-3-319-27674-8_16
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