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diff --git a/macros/evaluateImageRetrieval.sci b/macros/evaluateImageRetrieval.sci new file mode 100644 index 0000000..a8ac99d --- /dev/null +++ b/macros/evaluateImageRetrieval.sci @@ -0,0 +1,104 @@ +function [varargout] = evaluateImageRetrieval(image, IndexImage, ExpectedID, varargin) +// This function is used to evaluate the Image Search Results +// +// Calling Sequence +// averagePrecision = evaluateImageRetrieval(queryImage, ImageIndex, expectedIDs, Name, Value... ); +// [averagePrecision imageID Scores] = evaluateImageRetrieval(queryImage, ImageIndex, expectedIDs); +// +// Parameters +// queryImage: The query image, for which the similar image has to be found. Can be a grayscale or a RGB image +// ImageIndex: imageIndex object that contains the data set of all the images to be compared +// expectedIDs: A row or column vector containing the IDs of expected Similarity +// NumResults [Optional Input Argument]: Maximum number of results to be returned. Value: any integer (20 default) +// ROI [Optional Input Argument]: Query Image search region. Format [ x y width height ]. Default: [1 1 size(Image,2) size(Image,1)] +// averagePrecision: Average Precision Metric. Value in the range [0 1] +// imageID: M-by-1 vector consisting of Ranked Index of retrieved Images +// Scores: M-by-1 vector containing the similarity metric in the range 0 to 1 +// +// Description +// It returns the average precision metric for measuring the accuracy of image search results for the queryImage. +// +// Examples +// imgSet = imageSet(directory,'recursive'); +// [trainingSet testSet] = partition(imgSet,[0.8]); +// bag = bagOfFeatures(trainingSet); +// imageindex = indexImages(trainingSet, bag); +// queryImage = imread('sample.jpg'); +// imageIDs = retrieveImages(queryImage, imageindex); +// exp_id = [3 4 1 2]; /*For a 4 element image set*/ +// precision = evaluateImageRetreival(queryImage, imageindex, exp_id); +// +// Authors +// Umang Agrawal +// Rohit Suri + + /// varargout(1) = average_precision + /// varargout(2) = index + /// varargout(3) = score + [ lhs rhs ] = argn(0) + if rhs > 7 then + error(msprintf("Too many input arguments")) + end + + if lhs > 3 then + error(msprintf("Too many output arguments")) + end + image_list = mattolist(image) + + if lhs == 1 then + select rhs + case 3 then + + average_precision= opencv_evalutateImageRetrieval(image_list, IndexImage, ExpectedID) + + case 5 then + + average_precision = opencv_evaluateImageRetrieval(image_list, IndexImage, ExpectedID, varargin(1), varargin(2)) + + case 7 then + + average_precision = opencv_evaluateImageRetrieval(image_list, IndexImage, ExpectedID, varargin(1), varargin(2), varargin(3), varargin(4)) + end + + varargout(1) = average_precision + + elseif lhs == 2 then + select rhs + case 3 then + + [average_precision, index] = opencv_evalutateImageRetrieval(image_list, IndexImage, ExpectedID) + + case 5 then + + [average_precision, index] = opencv_evaluateImageRetrieval(image_list, IndexImage, ExpectedID, varargin(1), varargin(2)) + + case 7 then + + [average_precision, index] = opencv_evaluateImageRetrieval(image_list, IndexImage, ExpectedID, varargin(1), varargin(2), varargin(3), varargin(4)) + end + + varargout(1) = average_precision + varargout(2) = index + + elseif lhs == 3 then + select rhs + case 3 then + + [average_precision, index, score] = opencv_evalutateImageRetrieval(image_list, IndexImage, ExpectedID) + + case 5 then + + [average_precision, index, score] = opencv_evaluateImageRetrieval(image_list, IndexImage, ExpectedID, varargin(1), varargin(2)) + + case 7 then + + [average_precision, index, score] = opencv_evaluateImageRetrieval(image_list, IndexImage, ExpectedID, varargin(1), varargin(2), varargin(3), varargin(4)) + end + + varargout(1) = average_precision + varargout(2) = index + varargout(3) = score + + end + +endfunction |