ans = 3670

function [features, metrics] = exampleBagOfFeaturesColorExtractor(I) %颜色布局特征提取示例。专为bagfeatures使用。% %从真彩色图像中提取局部色彩布局特征，I和特征返回的%。特性的强度以%指标返回。(~ ~ P) = (I)大小;isColorImage = P == 3;将RGB图像转换为L*a*b* colorspace。L*a*b* colorspace %使您能够轻松量化颜色之间的视觉差异。%在L*a*b*颜色空间中，视觉上相似的颜色在L*a*b*值上有很小的%差异。Ilab = rgb2lab(我); % Compute the "average" L*a*b* color within 16-by-16 pixel blocks. The % average value is used as the color portion of the image feature. An % efficient method to approximate this averaging procedure over % 16-by-16 pixel blocks is to reduce the size of the image by a factor % of 16 using IMRESIZE. Ilab = imresize(Ilab, 1/16); % Note, the average pixel value in a block can also be computed using % standard block processing or integral images. % Reshape L*a*b* image into "number of features"-by-3 matrix. [Mr,Nr,~] = size(Ilab); colorFeatures = reshape(Ilab, Mr*Nr, []); % L2 normalize color features rowNorm = sqrt(sum(colorFeatures.^2,2)); colorFeatures = bsxfun(@rdivide, colorFeatures, rowNorm + eps); % Augment the color feature by appending the [x y] location within the % image from which the color feature was extracted. This technique is % known as spatial augmentation. Spatial augmentation incorporates the % spatial layout of the features within an image as part of the % extracted feature vectors. Therefore, for two images to have similar % color features, the color and spatial distribution of color must be % similar. % Normalize pixel coordinates to handle different image sizes. xnorm = linspace(-0.5, 0.5, Nr); ynorm = linspace(-0.5, 0.5, Mr); [x, y] = meshgrid(xnorm, ynorm); % Concatenate the spatial locations and color features. features = [colorFeatures y(:) x(:)]; % Use color variance as feature metric. metrics = var(colorFeatures(:,1:3),0,2); else % Return empty features for non-color images. These features are % ignored by bagOfFeatures. features = zeros(0,5); metrics = zeros(0,1); end

得分=5×10.4822 0.2143 0.1389 0.1384 0.1320