Dense and Sparse Labeling (DSL) with Multi-Dimensional Features for Saliency Detection

• This is the software for paper [1]. Please cite [1] if you use this code.
• Author: Yuchen Yuan
• Last updated: Oct 18, 2016

Installation

This software is implemented on MatConvNet [2] with CUDA 7.5 and cuDNN v3. CPU-only mode is also supported.

• Resources: The model files (required), together with the already-generated saliency maps on existing datasets, can be downloaded here
• Supported OS: This software is tested on 64-bit Ubuntu 14.04 and 64-bit Windows 8.1.
• MatConvNet: Please download MatConvNet to the current path, and compile with instructions. Below is a compilation example:

run matlab/vl_setupnn.m
vl_compilenn('enableGpu', true, 'cudaMethod', 'nvcc', ...
'cudaRoot', '/usr/local/cuda-7.5', ...
'enableCudnn', true, 'cudnnRoot', '/usr/local/cuda/');

• CUDA: If run with GPU, please download and install CUDA
• cuDNN: If run with GPU, please download and install cuDNN
• wine: If run under Linux, please install [wine](sudo apt-get install wine) for the SLIC program support.

Usage

• Entrance: Please run dsl_demo.m for an example use.
• Default input image path: image.
• Default trained network path: model.
• Default result path: result/1_DL for FCN results, result/2_SL for CCN results, and result/3_DC for DCN (final saliency map) results.
• GPU or CPU mode: Please set gpus = 1 for GPU mode, or gpus = [] for CPU-only mode.

Notes

• If an error in dagnn.BatchNorm occurs, please replace matconvnet/matlab/+dagnn/BatchNorm.m with support/BatchNorm.m
• If an error in dagnn.ReLU occurs, please replace matconvnet/matlab/+dagnn/ReLU.m with support/ReLU.m
• If a mex_link error is encountered while compiling MatConvNet, please try replacing the "parfor" with "for" in vl_compilenn.m. This issue is fixed in the latest version of MatConvNet.

References

[1] Y. Yuan, C. Li et al. "Dense and sparse labeling with multi-dimensional features for saliency detection", IEEE Trans. Circuits and Syst. Video Technol., vol. xx, no. xx, pp. xx-yy, Month. 2016

[2] A. Vedaldi and K. Lenc, "MatConvNet-convolutional neural networks for MATLAB", arXiv preprint arXiv:1412.4564, 2014.