Sub-Selective Quantization for Large-Scale Image Search


  • Yeqing Li University of Texas at Arlington
  • Chen Chen University of Texas at Arlington
  • Wei Liu IBM T. J. Watson Research Center
  • Junzhou Huang University of Texas at Arlington



image hashing, image quantization, binary encoding, image search, large-scale, sub-selective


Recently with the explosive growth of visual content on the Internet, large-scale image search has attracted intensive attention. It has been shown that mapping highdimensional image descriptors to compact binary codes can lead to considerable efficiency gains in both storage and similarity computation of images. However, most existing methods still suffer from expensive training devoted to large-scale binary code learning. To address this issue, we propose a sub-selection based matrix manipulation algorithm which can significantly reduce the computational cost of code learning. As case studies, we apply the sub-selection algorithm to two popular quantization techniques PCA Quantization (PCAQ) and Iterative Quantization (ITQ). Crucially, we can justify the resulting sub-selective quantization by proving its theoretic properties. Extensive experiments are carried out on three image benchmarks with up to one million samples, corroborating the efficacy of the sub-selective quantization method in terms of image retrieval.




How to Cite

Li, Y., Chen, C., Liu, W., & Huang, J. (2014). Sub-Selective Quantization for Large-Scale Image Search. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).