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Check out the latest superpixel benchmark — Superpixel Benchmark (2016) — and let me know your opinion! @david_stutz
05thAPRIL2016

READING

A. Babenko, A. Slesarev, A. Chigorin, V. S. Lempitsky. Neural codes for image retrieval. In Computer Vision, European Conference on, volume 8689 of Lecture Notes in Computer Science, pages 584–599, Zurich, Switzerland, September 2014. Springer.

Babenko et al. apply convolutional neural networks to image retrieval. In particular, the architecture proposed by Krizhevsky et al. [1] (shown in Figure 1), has been used to compute features for image retrieval. They experimented with different layers, dimensionality reduction techniques (e.g. PCA and Large-Margin Dimensionality Reduction [2]), and both the pre-trained model and a refined model to demonstrate state-of-the-art performance on many image retrieval datasets.

architecture

Figure 1 (click to enlarge): Architecture proposed in [1].

  • [1] A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, 2012.
  • [2] K. Simonyan, O. M. Parkhi, A. Vedaldi, and A. Zisserman. Fisher vector faces in the wild. In British Machine Vision Conference, 2013.

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below or using the following platforms: