The code are for the grand challenge Salient360! at ICME 2017. Two computational models have been implemented in the code, which are:
- Head motion based saliency model (Model type 1); and
- Head and eye-motion based saliency model (Model type 2).
The corresponding functions for model types 1 and 2 are HeadSalMap
and
HeadEyeSalMap
, respectively.
The approach is based on our previous publication [1], which employs a stacked auto-encoder-based reconstruction framework.
To use the code, one need to do the following steps:
- Decompress the file
salient360_XDQS.tar.gz
to a folder<saliency_source>
. - Create two sub-folders,
images
andsaliency
under<saliency_source>
. - Move images to be processed to the folder
<saliency_source>/images
. - Execute the matlab script
process.m
with command linematlab < process.m
. - Enter the folder
<saliency_source>/saliency
to check for results.
After the execution of the script, results will be stored in
<saliency_source>/saliency
. The suffix for model types 1 and 2 are _SH
and _SHE
, respectively. For instance, two files, P10_SH.bin
and
P10_SHE.bin
, will be generated after processing the image P10.jpg
.
The following three files are the major entries to the functions:
-
processing.m
: This matlab script for processing all images under the folderimages
. -
HeadSalMap.m
: This file implements the functionHeadSalMap
, which estimates the saliency map of model type 1. Its input and output arguments are:imgIn
: the input equirectangular image organized in an RGB, withsize(imgIn)
being[Height,Width,3]
.matOut
: the output "double" matrix having the saliency values. Its size is[Height,Width]
-
HeadEyeSalMap.m
: This file implements the functionHeadEyeSalMap
, which estimates the saliency of model type 2. Its input and output arguments are:imgIn
: the input equirectangular image organized in an RGB, withsize(imgIn)
being[Height,Width,3]
.matOut
: the output "double" matrix having the saliency values. Its size is[Height,Width]
Our team can be referenced as xd_qsal, with team members listed as below:
- Fei Qi (齐飞)
- Chunhuan Lin (林春焕)
- Zhaohui Xia (夏朝辉)
- Shuai Gao (高帅)
- Hao Li (李昊)
- Chen Xia (夏辰)
- Guangming Shi (石光明)
[1] Chen Xia, Fei Qi, Guangming Shi, "Bottom-up Visual Saliency Estimation with Deep Autoencoder-based Sparse Reconstruction," IEEE Transactions on Neural Networks and Learning Systems, 27(6): 1227–1240, June 2016. doi: 10.1109/TNNLS.2015.2512898