显著性学习-FT显著性算法

FT显著性

作者主页
作者的算法流程图

作者主要利用的是颜色特征和亮度特征。
1. 对图像img进行高斯滤波得到imgrgb。
2. 将图像imgrgb由RGB颜色空间转换为LAB颜色空间imglab。
3. 对转换后的图像imglab 的L,A,B三个通道的图像分别取均值得到lm,am,bm。
4. 对分别对三个通道的均值图像和滤波得到的图像取欧氏距离并求和。

%---------------------------------------------------------
% Copyright (c) 2009 Radhakrishna Achanta [EPFL]
% Contact: [email protected]
%---------------------------------------------------------
% Citation:
% @InProceedings{LCAV-CONF-2009-012,
%    author      = {Achanta, Radhakrishna and Hemami, Sheila and Estrada,
%                  Francisco and Süsstrunk, Sabine},
%    booktitle   = {{IEEE} {I}nternational {C}onference on {C}omputer
%                  {V}ision and {P}attern {R}ecognition},
%    year        = 2009
% }
%---------------------------------------------------------
% Please note that the saliency maps generated using this
% code may be slightly different from those of the paper.
% This seems to be because the RGB to Lab conversion is
% different from the one used for the results in the C++ code.
% The C++ code is available on the same page as this matlab
% code (http://ivrg.epfl.ch/supplementary_material/RK_CVPR09/index.html)
% One should preferably use the C++ as reference and use
% this matlab implementation mostly as proof of concept
% demo code.
%---------------------------------------------------------
%
%
%---------------------------------------------------------
% Read image and blur it with a 3x3 or 5x5 Gaussian filter
%---------------------------------------------------------
img = imread('input_image.jpg');%Provide input image path
gfrgb = imfilter(img, fspecial('gaussian', 3, 3), 'symmetric', 'conv');
%---------------------------------------------------------
% Perform sRGB to CIE Lab color space conversion (using D65)
%---------------------------------------------------------
cform = makecform('srgb2lab', 'whitepoint', whitepoint('d65'));
lab = applycform(gfrgb,cform);
%---------------------------------------------------------
% Compute Lab average values (note that in the paper this
% average is found from the unblurred original image, but
% the results are quite similar)
%---------------------------------------------------------
l = double(lab(:,:,1)); lm = mean(mean(l));
a = double(lab(:,:,2)); am = mean(mean(a));
b = double(lab(:,:,3)); bm = mean(mean(b));
%---------------------------------------------------------
% Finally compute the saliency map and display it.
%---------------------------------------------------------
sm = (l-lm).^2 + (a-am).^2 + (b-bm).^2;
imshow(sm,[]);
%---------------------------------------------------------