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E2BoWs: An End-to-End Bag-of-Words Model via Deep Convolutional Neural Network for Image Retrieval

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We provide the source code for paper "E2BoWs: End-to-End Bag-of-Words Model vid Deep Convolutional Neural Network" in ChinaMM'17. Paper is also accepted by Neurocomputing. If you find our work helpful, please kindly cite our paper:

@article{E2BoWs,
title={E2BoWs: An End-to-End Bag-of-Words Model via Deep Convolutional Neural Network for Image Retrieval},
author={Liu, Xiaobin. and Zhang, Shiliang. and Huang, Tiejun. and Tian, Qi.},
journal={Neurocomputing},
year={2019} }

We present some new layers such as "ReduceLayer", "SimED2LossLayer" and "ConvReLULayer".

We also present a tool to extract features: "my_extract_features", which writes features in binary files. A set of scripts is presented to read the features and to test the performance. An example of how to use the tools and scripts is shown in folder "CIFAR-10".

Please feel free to contact me if you have any question.

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E2BoWs: An End-to-End Bag-of-Words Model via Deep Convolutional Neural Network for Image Retrieval

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