Source code for our CVPR 2015 work on saliency detection by multi-context deep learning.
Created by Rui Zhao, on May 21, 2015
This source code is mainly written in Python and bash shell scripts, and it is for the following paper:
- Rui Zhao, Wanli Ouyang, Hongsheng Li, and Xiaogang Wang. Saliency Detection by Multi-Context Deep Learning. In CVPR 2015.
- Supported OS: this source code was tested on 64-bit Arch Linux OS, and it should also be executable in other linux distributions.
- Pre-installations: refer to caffe for
packages required by caffe toolkit. Packages requried by Python scripts include
- skimage
- leveldb
- matplotlib
- Download caffe models: cd models/ && sh get_models.sh && cd .. (or you can download manually via Baidu Yun: http://pan.baidu.com/s/1sjoP8Ln Password: enn9)
- Customize test images: put your test images in folder ./images, or revise the test_folder in get_deep_mutlicontext_saliency.sh to your customized image folder.
- Run demo in bash shell:
sh get_deep_mutlicontext_saliency.sh
-
Caffe-sal is a customized version of original caffe toolkit. Comparing the original version, revisions happen in the following files:
- ./caffe-sal/src/caffe/layers/mcwindowdatalayers.cpp
- ./caffe-sal/src/caffe/layers/mcwindowdatalayers.cu
- ./caffe-sal/src/caffe/proto/caffe.proto
- ./caffe-sal/src/caffe/layer_factory.cpp
- ./caffe-sal/include/caffe/data_layers.hpp
-
Test folder can be set in ./get_deep_multicontext_saliency.sh
-
This source code requires GPU to accelerate the testing process
-
If everything runs correctly, it will generate resulting saliency maps in test folder (./images), suffix _sc means results produced by single-context deep model, and _mc by multi-context deep model.
##Citing our work Please kindly cite our work in your publications if it helps your research:
@inproceedings{zhao2015saliency,
title = {Saliency Detection by Multi-Context Deep Learning},
author={Zhao, Rui and Ouyang, Wanli and Li, Hongsheng and Wang, Xiaogang},
booktitle = {IEEE Conference on Computer Vision and Pattern
Recognition (CVPR)},
year = {2015}
}
##License
Copyright (c) 2015, Rui Zhao
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in
the documentation and/or other materials provided with the distribution
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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