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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
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