ans = 3670

函数(功能、指标)= exampleBagOfFeaturesColorExtractor (I) %特征提取器的颜色布局示例。为使用bagOfFeatures而设计的。% %地方色彩布局从真彩图像中提取的特征,我和%返回功能。中返回%的强度特性指标。% 2014 - 2020版权MathWorks公司(~ ~ P) = (I)大小;isColorImage = P = = 3;如果isColorImage %将RGB图像转换成L * a * b *色彩。L * a * b *色彩%使您能够轻松地量化颜色之间的视觉差异。%视觉相似的颜色在L * a * b *色彩会有小% L * a * b *值的差异。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

成绩=5×10.4776 0.2138 0.1386 0.1382 0.1317