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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