ANS = 3670

函数[特征，度量] = exampleBagOfFeaturesColorExtractor（I）％实施例颜色布局特征抽取。设计用于bagOfFeatures使用。％％是从真彩色图像提取的局部颜色布局特征，I和％在功能返回。该特征的强度在％的指标返回。[〜，〜，P] =尺寸（I）;isColorImage = P == 3;如果isColorImage％转换RGB图像到的L * a * b *表色空间。所述的L * a * b *表色空间％使您轻松地量化颜色之间的视觉差异。％视觉相似颜色的L * A * B *色彩空间将在其的L * a * b *值小％的差异。ILAB = rgb2lab（I）; % 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

分数=5×10.4822 0.2143 0.1389 0.1384 0.1320