References

• 1. 1)
     • 5. Wang, J., Song, J., Xu, X., Shen, H.T., Li, S.: ‘Optimized cartesian k-means’, IEEE Trans. Knowl. Data Eng., 2015, 27, (1), pp. 180–192 (doi: 10.1109/TKDE.2014.2324592).
  2. 2)
     • 6. Babenko, A., Lempitsky, V.S.: ‘Additive quantization for extreme vector compression’. IEEE Conf. on CVPR, Columbus, OH, USA, June 2014, pp. 931–938.
  3. 3)
     • 4. Yunchao, G., Lazebnik, S., Gordo, A., et al: ‘Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (12), pp. 2916–2929 (doi: 10.1109/TPAMI.2012.193).
  4. 4)
     • 4. Ge, T., He, K., Ke, Q., Sun, J.: ‘Optimized product quantization’, IEEE Trans. Pattern Anal. Mach. Intell, 2014, 36, (4), pp. 744–755 (doi: 10.1109/TPAMI.2013.240).
  5. 5)
     • 2. Guo, Y., Ding, G., Jin, X., Wang, J.: ‘Learning predictable and discriminative attributes for visual recognition’. AAAI Conference on Artificial Intelligence, Austin, TX, USA, January 2015, pp. 3783–3789.
  6. 6)
     • 3. Heo, J.P., Lee, Y., He, J., Chang, S.F., Yoon, S.E.: ‘Spherical hashing’. IEEE Conference on CVPR, Rhode Island, USA, June 2012, pp. 2957–2964.
  7. 7)
     • 1. Shakhnarovich, G., Darrell, T., Indyk, P.: ‘Nearest neighbor methods in learning and vision: theory and practice’ (MIT press, Cambridge, MA, USA, 2015).