作為方便、快捷、功能強(qiáng)大的開發(fā)工具，Matlab有大量的第三方資源。在圖像處理、計(jì)算機(jī)視覺方面，幾年前的Matlab功能不如現(xiàn)在強(qiáng)大，所以在matahworks網(wǎng)站的File exchange（File exchange ）可以看到大量的個(gè)人上傳的代碼，但絕大多數(shù)綜合性和運(yùn)算性能不強(qiáng)。vlfeat的出現(xiàn)改變了這一現(xiàn)狀，可以移步官網(wǎng)下載vlfeat ，最好下載編譯過的bin文件，否則只下載源碼的話需要自己編譯。不過官方的編譯不太完整，某些函數(shù)如果要在64的Windows10或Windows7下使用，需要自己編譯，也可單獨(dú)對(duì)某個(gè)函數(shù)使用mex命令編譯，我使用Visual Studio 2010和Visual Studio 2015編譯過。
vlfeat的功能很多，包含了多種特征提取（SIFT、DSIFT、QuickSIFT、PHOW、HOG、MSER、SLIC、Fisher、LBP）、局部特征匹配（UBC match）、分類（SVM、GMM）、聚類（IKM、HKM、AIB，Agglomerative Information Bottleneck）、檢索（Random kd-tree）、分割、計(jì)算評(píng)價(jià)指標(biāo)（true positives and false positives）、作圖（Precision-recall curve、ROC curve）、生成分布函數(shù)（Second derivative of the Gaussian density function、Derivative of the Gaussian density function、Derivative of the sigmoid function、Standard Gaussian density function、Sigmoid function）、特征編碼（VLAD）等功能，可參看幫助文章或演示程序。
注意，在運(yùn)行演示程序前，要先運(yùn)行toolbox目錄下的vl_setup.m和vl_root.m，以添加必要的路徑。
這里介紹一下Caltech101的分類演示程序phow_caltech101.m。

function phow_caltech101()% PHOW_CALTECH101 Image classification in the Caltech-101 dataset% This program demonstrates how to use VLFeat to construct an image% classifier on the Caltech-101 data. The classifier uses PHOW% features (dense SIFT), spatial histograms of visual words, and a% Chi2 SVM. To speedup computation it uses VLFeat fast dense SIFT,% kd-trees, and homogeneous kernel map. The program also% demonstrates VLFeat PEGASOS SVM solver, although for this small% dataset other solvers such as LIBLINEAR can be more efficient.% % Author: Andrea Vedaldi% Copyright (C) 2011-2013 Andrea Vedaldi% All rights reserved. conf.calDir = 'data/caltech-101' ;conf.dataDir = 'data/' ;conf.autoDownloadData = true ;conf.numTrain = 15 ;conf.numTest = 15 ;conf.numClasses = 102 ;conf.numWords = 600 ;conf.numSpatialX = [2 4] ;conf.numSpatialY = [2 4] ;conf.quantizer = 'kdtree' ;conf.svm.C = 10 ;conf.svm.solver = 'liblinear' ;conf.svm.biasMultiplier = 1 ;conf.phowOpts = {'Step', 3} ;conf.clobber = false ;conf.tinyProblem = true ;conf.prefix = 'baseline' ;conf.randSeed = 1 ;%為加快速度，只處理5類數(shù)據(jù)，詞典單詞數(shù)為300if conf.tinyProblemconf.prefix = 'tiny' ;conf.numClasses = 5 ;conf.numSpatialX = 2 ;conf.numSpatialY = 2 ;conf.numWords = 300 ;conf.phowOpts = {'Verbose', 2, 'Sizes', 7, 'Step', 5} ;end%設(shè)置詞典、直方圖、模型、運(yùn)行結(jié)果、特征文件存儲(chǔ)路徑conf.vocabPath = fullfile(conf.dataDir, [conf.prefix '-vocab.mat']) ;conf.histPath = fullfile(conf.dataDir, [conf.prefix '-hists.mat']) ;conf.modelPath = fullfile(conf.dataDir, [conf.prefix '-model.mat']) ;conf.resultPath = fullfile(conf.dataDir, [conf.prefix '-result']) ;conf.featPath = fullfile(conf.dataDir, [conf.prefix '-feat.mat']) ;%設(shè)置隨機(jī)數(shù)生成器randn('state',conf.randSeed) ;vl_twister('state',conf.randSeed) ;%第一次運(yùn)行需下載數(shù)據(jù)，如速度慢，可使用迅雷在該網(wǎng)址手動(dòng)下載if ~exist(conf.calDir, 'dir') || ...(~exist(fullfile(conf.calDir, 'airplanes'),'dir') && ...~exist(fullfile(conf.calDir, '101_ObjectCategories', 'airplanes')))if ~conf.autoDownloadDataerror(...['Caltech-101 data not found. ' ...'Set conf.autoDownloadData=true to download the required data.']) ;endvl_xmkdir(conf.calDir) ;calUrl = ['http://www.vision.caltech.edu/Image_Datasets/' ...'Caltech101/101_ObjectCategories.tar.gz'] ;fprintf('Downloading Caltech-101 data to ''%s''. This will take a while.', conf.calDir) ;untar(calUrl, conf.calDir) ;end%設(shè)置圖像集路徑if ~exist(fullfile(conf.calDir, 'airplanes'),'dir')conf.calDir = fullfile(conf.calDir, '101_ObjectCategories') ;end%獲取類別信息，文件夾名字為類別名字classes = dir(conf.calDir) ;classes = classes([classes.isdir]) ;classes = {classes(3:conf.numClasses+2).name} ;images = {} ;imageClass = {} ;%遍歷各圖像集各類文件夾，獲取圖像名和類別名for ci = 1:length(classes)ims = dir(fullfile(conf.calDir, classes{ci}, '*.jpg'))' ; ims = vl_colsubset(ims, conf.numTrain + conf.numTest) ;ims = cellfun(@(x)fullfile(classes{ci},x),{ims.name},'UniformOutput',false) ;images = {images{:}, ims{:}} ;imageClass{end+1} = ci * ones(1,length(ims)) ;end%建立訓(xùn)練集selTrain = find(mod(0:length(images)-1, conf.numTrain+conf.numTest) < conf.numTrain) ;%建立測(cè)試集selTest = setdiff(1:length(images), selTrain) ;imageClass = cat(2, imageClass{:}) ;model.classes = classes ;model.phowOpts = conf.phowOpts ;model.numSpatialX = conf.numSpatialX ;model.numSpatialY = conf.numSpatialY ;model.quantizer = conf.quantizer ;model.vocab = [] ;model.w = [] ;model.b = [] ;model.classify = @classify ;%提取圖像PHOW特征并訓(xùn)練詞典if ~exist(conf.vocabPath) || conf.clobber%取30幅圖像進(jìn)行訓(xùn)練selTrainFeats = vl_colsubset(selTrain, 30) ;descrs = {} ;for ii = 1:length(selTrainFeats)im = imread(fullfile(conf.calDir, images{selTrainFeats(ii)})) ;%對(duì)圖像進(jìn)行標(biāo)準(zhǔn)化im = standarizeImage(im) ;%提取特征[drop, descrs{ii}] = vl_phow(im, model.phowOpts{:}) ;enddescrs = vl_colsubset(cat(2, descrs{:}), 10e4) ;descrs = single(descrs) ;save(conf.featPath, 'descrs') ;% 對(duì)特征進(jìn)行聚類生成詞典vocab = vl_kmeans(descrs, conf.numWords, 'verbose', 'algorithm', 'elkan', MaxNumIterations', 50) ; save(conf.vocabPath, 'vocab') ;elseload(conf.vocabPath) ;endmodel.vocab = vocab ;if strcmp(model.quantizer, 'kdtree')%為詞典建立kdtree索引model.kdtree = vl_kdtreebuild(vocab) ;end%計(jì)算圖像直方圖if ~exist(conf.histPath) || conf.clobberhists = {} ;parfor ii = 1:length(images) fprintf('Processing %s (%.2f %%)\n', images{ii}, 100 * ii / length(images)) ;im = imread(fullfile(conf.calDir, images{ii})) ;hists{ii} = getImageDescriptor(model, im);endhists = cat(2, hists{:}) ;save(conf.histPath, 'hists') ;elseload(conf.histPath) ;end% 對(duì)直方圖進(jìn)行homker映射psix = vl_homkermap(hists, 1, 'kchi2', 'gamma', .5) ;% 訓(xùn)練詞典if ~exist(conf.modelPath) || conf.clobberswitch conf.svm.solvercase {'sgd', 'sdca'}lambda = 1 / (conf.svm.C * length(selTrain)) ;w = [] ;parfor ci = 1:length(classes)perm = randperm(length(selTrain)) ;fprintf('Training model for class %s\n', classes{ci}) ;y = 2 * (imageClass(selTrain) == ci) - 1 ;[w(:,ci) b(ci) info] = vl_svmtrain(psix(:, selTrain(perm)), y(perm), lambda, ...'Solver', conf.svm.solver,'MaxNumIterations', 50/lambda, ...'BiasMultiplier', conf.svm.biasMultiplier, 'Epsilon', 1e-3);endcase 'liblinear'svm = train(imageClass(selTrain)',sparse(double(psix(:,selTrain))), ...sprintf(' -s 3 -B %f -c %f', conf.svm.biasMultiplier, conf.svm.C),'col') ;w = svm.w(:,1:end-1)' ;b = svm.w(:,end)' ; endmodel.b = conf.svm.biasMultiplier * b ;model.w = w ;save(conf.modelPath, 'model') ;elseload(conf.modelPath) ;end% 計(jì)算測(cè)試圖像得分scores = model.w' * psix + model.b' * ones(1,size(psix,2)) ;[drop, imageEstClass] = max(scores, [], 1) ;% 計(jì)算混淆矩陣idx = sub2ind([length(classes), length(classes)], ...imageClass(selTest), imageEstClass(selTest)) ;confus = zeros(length(classes)) ;confus = vl_binsum(confus, ones(size(idx)), idx) ;% Plotsfigure(1) ; clf;subplot(1,2,1) ;imagesc(scores(:,[selTrain selTest])) ; title('Scores') ;set(gca, 'ytick', 1:length(classes), 'yticklabel', classes) ;subplot(1,2,2) ;imagesc(confus) ;title(sprintf('Confusion matrix (%.2f %% accuracy)', ...100 * mean(diag(confus)/conf.numTest) )) ;print('-depsc2', [conf.resultPath '.ps']) ;save([conf.resultPath '.mat'], 'confus', 'conf') ;% -------------------------------------------------------------------------function im = standarizeImage(im)% -------------------------------------------------------------------------im = im2single(im) ;if size(im,1) > 480, im = imresize(im, [480 NaN]) ; end% -------------------------------------------------------------------------function hist = getImageDescriptor(model, im)% -------------------------------------------------------------------------im = standarizeImage(im) ;width = size(im,2) ;height = size(im,1) ;numWords = size(model.vocab, 2) ; [frames, descrs] = vl_phow(im, model.phowOpts{:}) ; switch model.quantizercase 'vq'[drop, binsa] = min(vl_alldist(model.vocab, single(descrs)), [], 1) ;case 'kdtree'binsa = double(vl_kdtreequery(model.kdtree, model.vocab,single(descrs), 'MaxComparisons', 50)) ;endfor i = 1:length(model.numSpatialX)binsx = vl_binsearch(linspace(1,width,model.numSpatialX(i)+1), frames(1,:)) ;binsy = vl_binsearch(linspace(1,height,model.numSpatialY(i)+1), frames(2,:)) ; bins = sub2ind([model.numSpatialY(i), model.numSpatialX(i), numWords], binsy,binsx,binsa) ;hist = zeros(model.numSpatialY(i) * model.numSpatialX(i) * numWords, 1) ;hist = vl_binsum(hist, ones(size(bins)), bins) ;hists{i} = single(hist / sum(hist)) ;endhist = cat(1,hists{:}) ;hist = hist / sum(hist) ;% -------------------------------------------------------------------------function [className, score] = classify(model, im)% -------------------------------------------------------------------------hist = getImageDescriptor(model, im) ;psix = vl_homkermap(hist, 1, 'kchi2', 'gamma', .5) ;scores = model.w' * psix + model.b' ;[score, best] = max(scores) ;className = model.classes{best} ;

Matlab版本的函數(shù)vlfeat可在該頁查看，API函數(shù) 。簡(jiǎn)單列舉如下：

• vl_compile Compile VLFeat MEX files
  vl_demo Run VLFeat demos
  vl_harris Harris corner strength
  vl_help VLFeat toolbox builtin help
  vl_noprefix Create a prefix-less version of VLFeat commands
  vl_root Obtain VLFeat root path
  vl_setup Add VLFeat Toolbox to the path

• AIB
  vl_aib Agglomerative Information Bottleneck
  vl_aibcut Cut VL_AIB tree
  vl_aibcuthist Compute a histogram by using an AIB compressed alphabet
  vl_aibcutpush Quantize based on VL_AIB cut
  vl_aibhist Compute histogram over VL_AIB tree
• FISHER
  vl_fisher Fisher vector feature encoding
• GEOMETRY
  vl_hat Hat operator
  vl_ihat Inverse vl_hat operator
  vl_irodr Inverse Rodrigues' formula
  vl_rodr Rodrigues' formula
• GMM
  vl_gmm Learn a Gaussian Mixture Model using EM
• IMOP
  vl_dwaffine Derivative of an affine warp
  vl_imarray Flattens image array
  vl_imarraysc Scale and flattens image array
  vl_imdisttf Image distance transform
  vl_imdown Downsample an image by two
  vl_imgrad Image gradient
  vl_imintegral Compute integral image
  vl_impattern Generate an image from a stock pattern
  vl_imreadbw Reads an image as gray-scale
  vl_imreadgray Reads an image as gray-scale
  vl_imsc Scale image
  vl_imsmooth Smooth image
  vl_imup Upsample an image by two
  vl_imwbackward Image backward warping
  vl_imwhiten Whiten an image
  vl_rgb2xyz Convert RGB color space to XYZ
  vl_tps Compute the thin-plate spline basis
  vl_tpsu Compute the U matrix of a thin-plate spline transformation
  vl_waffine Apply affine transformation to points
  vl_witps Inverse thin-plate spline warping
  vl_wtps Thin-plate spline warping
  vl_xyz2lab Convert XYZ color space to LAB
  vl_xyz2luv Convert XYZ color space to LUV
  vl_xyz2rgb Convert XYZ to RGB
• KMEANS
  vl_hikmeans Hierachical integer K-means
  vl_hikmeanshist Compute histogram of quantized data
  vl_hikmeanspush Push data down an integer K-means tree
  vl_ikmeans Integer K-means
  vl_ikmeanshist Compute histogram of quantized data
  vl_ikmeanspush Project data on integer K-means paritions
  vl_kmeans Cluster data using k-means
• MISC
  vl_alldist2 Pairwise distances
  vl_alphanum Sort strings using the Alphanum algorithm
  vl_argparse Parse list of parameter-value pairs
  vl_binsearch Maps data to bins
  vl_binsum Binned summation
  vl_colsubset Select a given number of columns
  vl_cummax Cumulative maximum
  vl_getpid Get MATLAB process ID
  vl_grad Compute the gradient of an image
  vl_histmarg Marginal of histogram
  vl_hog Compute HOG features
  vl_homkermap Homogeneous kernel map
  vl_ihashfind Find labels in an integer hash table
  vl_ihashsum Accumulate integer labels into a hash table
  vl_inthist Calculate Integral Histogram
  vl_isoctave Determines whether Octave is running
  vl_kdtreebuild Build randomized kd-tree
  vl_kdtreequery Query KD-tree
  vl_lbp Local Binary Patterns
  vl_lbpfliplr Flip LBP features left-right
  vl_localmax Find local maximizers
  vl_matlabversion Return MATLAB version as an integer
  vl_numder Numerical derivative
  vl_numder2 Numerical second derivative
  vl_override Override structure subset
  vl_pegasos [deprecated]
  vl_sampleinthist Sample integral histogram
  vl_simdctrl Toggle VLFeat SIMD optimizations
  vl_svmdataset Construct advanced SVM dataset structure
  vl_svmpegasos [deprecated]
  vl_svmtrain Train a Support Vector Machine
  vl_threads Control VLFeat computational threads
  vl_twister Random number generator
  vl_version Obtain VLFeat version information
  vl_whistc Weighted histogram
  vl_xmkdir Create a directory recursively.
• MSER
  vl_erfill Fill extremal region
  vl_ertr Transpose exremal regions frames
  vl_mser Maximally Stable Extremal Regions
• PLOTOP
  vl_cf Creates a copy of a figure
  vl_click Click a point
  vl_clickpoint Select a point by clicking
  vl_clicksegment Select a segment by clicking
  vl_det Compute DET curve
  vl_figaspect Set figure aspect ratio
  vl_linespec2prop Convert PLOT style line specs to line properties
  vl_plotbox Plot boxes
  vl_plotframe Plot a geometric frame
  vl_plotgrid Plot a 2-D grid
  vl_plotpoint Plot 2 or 3 dimensional points
  vl_plotstyle Get a plot style
  vl_pr Precision-recall curve.
  vl_printsize Set the printing size of a figure
  vl_roc ROC curve.
  vl_tightsubplot Tiles axes without wasting space
  vl_tpfp Compute true positives and false positives
• QUICKSHIFT
  vl_flatmap Flatten a tree, assigning the label of the root to each node
  vl_imseg Color an image based on the segmentation
  vl_quickseg Produce a quickshift segmentation of a grayscale or color image
  vl_quickshift Quick shift image segmentation
  vl_quickvis Create an edge image from a Quickshift segmentation.
• SIFT
  vl_covdet Covariant feature detectors and descriptors
  vl_dsift Dense SIFT
  vl_frame2oell Convert a geometric frame to an oriented ellipse
  vl_liop Local Intensity Order Pattern descriptor
  vl_phow Extract PHOW features
  vl_plotsiftdescriptor Plot SIFT descriptor
  vl_plotss Plot scale space
  vl_sift Scale-Invariant Feature Transform
  vl_siftdescriptor Raw SIFT descriptor
  vl_ubcmatch Match SIFT features
  vl_ubcread Read Lowe's SIFT implementation data files
• SLIC
  vl_slic SLIC superpixels
• SPECIAL
  vl_ddgaussian Second derivative of the Gaussian density function
  vl_dgaussian Derivative of the Gaussian density function
  vl_dsigmoid Derivative of the sigmoid function
  vl_gaussian Standard Gaussian density function
  vl_rcos RCOS function
  vl_sigmoid Sigmoid function
• VLAD
  vl_vlad VLAD feature encoding

具體使用方法可運(yùn)行演示程序。

轉(zhuǎn)載于:https://blog.51cto.com/8764888/2053993

總結(jié)

以上是生活随笔為你收集整理的Matlab图像识别/检索系列(8)—开源工具介绍之vlfeat的全部?jī)?nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。