前言
所有的例图均为下图所示计算得来的，是《比较几种常见的图像特征》
颜色特征：
clc clear %% I = imread('1.jpg'); hsv_I = rgb2hsv(I); %% I_h = hsv_I(:,:,1); I_s = hsv_I(:,:,2); I_v = hsv_I(:,:,3); %% mean_hsv_I = [mean(I_h) mean(I_s) mean(I_v)]; % 一阶 %% std_hsv_I = [std(I_h) std(I_s) std(I_v)];% 二阶 %% skewness_hsv_I = [skewness(I_h) skewness(I_s) skewness(I_v)];% 三阶
纹理特征：
SIFT:
可参考经典demo: [Keypoint detector (ubc.ca)](https://www.cs.ubc.ca/~lowe/keypoints/)
HOG:
clear img = imread('1.jpg'); [featureVector,hogVisualization] = extractHOGFeatures(img); figure; imshow(img); hold on; plot(hogVisualization);
LBP:
clc clear I = imread('1.jpg'); I = rgb2gray(I); lbpFeatures = extractLBPFeatures(I,'CellSize',[32 32],'Normalization','None'); numNeighbors = 8; numBins = numNeighbors*(numNeighbors-1)+3; lbpCellHists = reshape(lbpFeatures,numBins,[]); lbpCellHists = bsxfun(@rdivide,lbpCellHists,sum(lbpCellHists)); lbpFeatures = reshape(lbpCellHists,1,[]);
