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| author | shamikam | 2017-01-16 02:56:17 +0530 |
|---|---|---|
| committer | shamikam | 2017-01-16 02:56:17 +0530 |
| commit | a6df67e8bcd5159cde27556f4f6a315f8dc2215f (patch) | |
| tree | e806e966b06a53388fb300d89534354b222c2cad /macros/bagOfFeatures.sci | |
| download | FOSSEE_Image_Processing_Toolbox-master.tar.gz FOSSEE_Image_Processing_Toolbox-master.tar.bz2 FOSSEE_Image_Processing_Toolbox-master.zip | |
Diffstat (limited to 'macros/bagOfFeatures.sci')
| -rw-r--r-- | macros/bagOfFeatures.sci | 50 |
1 files changed, 50 insertions, 0 deletions
diff --git a/macros/bagOfFeatures.sci b/macros/bagOfFeatures.sci new file mode 100644 index 0000000..480d775 --- /dev/null +++ b/macros/bagOfFeatures.sci @@ -0,0 +1,50 @@ +function [bag]=bagOfFeatures(imageSet,varargin) +// This function is used to create a Bag-of-Words model (BoW model). +// +// Calling Sequence +// bag = bagOfFeatures(imgSet); +// bag = bagOfFeatures(imgSet, Name, Value, ...); +// +// Parameters +// bag: Bag of visual words +// imgSet: An imageSet structure +// VocabularySize: (Optional) Number of visual words (Default- 500) +// StrongestFeatures: (Optional) Fraction of strongest features (Default- 1.0) +// Upright: (Optional) Orientation of SURF feature vector (Default- true) +// Verbose: (Optional) Enable progress display to screen (Default- true) +// +// Description +// BoW model is applied to classify images by treating image features as words. The bagOfFeatures object contains a bag of visual words that help in image classification. +// +// Examples +// imgSet = imageSet(directory,'recursive'); +// [trainingSet testSet] = partition(imgSet,[0.8]); +// bag = bagOfFeatures(trainingSet); +// +// Authors +// Rohit Suri +// Umang Agrawal + + [lhs rhs]=argn(0); + if lhs>1 + error(msprintf(" Too many output arguments")); + elseif rhs<1 + error(msprintf("At least one argument is required")); + elseif rhs>9 + error(msprintf(" Too many input arguments")); + end + imageSetList=imageSetToList(imageSet); + select rhs + case 1 then + bagList=opencv_bagOfFeatures(imageSetList); + case 3 then + bagList=opencv_bagOfFeatures(imageSetList,varargin(1),varargin(2)); + case 5 then + bagList=opencv_bagOfFeatures(imageSetList,varargin(1),varargin(2),varargin(3),varargin(4)); + case 7 then + bagList=opencv_bagOfFeatures(imageSetList,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6)); + case 9 then + bagList=opencv_bagOfFeatures(imageSetList,varargin(1),varargin(2),varargin(3),varargin(4),varargin(5),varargin(6),varargin(7),varargin(8)); + end + bag=struct('FilePath',bagList(2)(1,1),'VocabularySize',bagList(3)(1,1),'StrongestFeatures',bagList(4)(1,1),'Upright',bagList(5)(1,1)); +endfunction |