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Raginsky, M. and Lazebnik, S. (2009) Locality-Sensitive Binary Codes from Shift- Invariant Kernels. In: Bengio, Y., Schuurmans, D., Lafferty, J.D., Williams, C.K.I. and Culotta, A., Eds. Advances in Neural Infor-mation Processing Systems 22, Curran Associates, Inc., 1509-1517.

has been cited by the following article:

• TITLE: Image Retrieval Using Deep Convolutional Neural Networks and Regularized Locality Preserving Indexing Strategy

  AUTHORS: Xiaoxiao Ma, Jiajun Wang

  KEYWORDS: Image Retrieval, CNN, RLPI, Hash

  JOURNAL NAME: Journal of Computer and Communications, Vol.5 No.3, March 13, 2017

  ABSTRACT: Convolutional Neural Networks (CNN) has been a very popular area in large scale data processing and many works have demonstrate that CNN is a very promising tool in many field, e.g., image classification and image retrieval. Theoretically, CNN features can become better and better with the increase of CNN layers. But on the other side more layers can dramatically increase the computational cost on the same condition of other devices. In addition to CNN features, how to dig out the potential information contained in the features is also an important aspect. In this paper, we propose a novel approach utilize deep CNN to extract image features and then introduce a Regularized Locality Preserving Indexing (RLPI) method which can make features more differentiated through learning a new space of the data space. First, we apply deep networks (VGG-net) to extract image features and then introduce Regularized Locality Preserving Indexing (RLPI) method to train a model. Finally, the new feature space can be generated through this model and then can be used to image retrieval.