001/**
002 * Copyright (c) 2011, The University of Southampton and the individual contributors.
003 * All rights reserved.
004 *
005 * Redistribution and use in source and binary forms, with or without modification,
006 * are permitted provided that the following conditions are met:
007 *
008 *   *  Redistributions of source code must retain the above copyright notice,
009 *      this list of conditions and the following disclaimer.
010 *
011 *   *  Redistributions in binary form must reproduce the above copyright notice,
012 *      this list of conditions and the following disclaimer in the documentation
013 *      and/or other materials provided with the distribution.
014 *
015 *   *  Neither the name of the University of Southampton nor the names of its
016 *      contributors may be used to endorse or promote products derived from this
017 *      software without specific prior written permission.
018 *
019 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
020 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
021 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
022 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
023 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
024 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
025 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
026 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
027 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
028 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
029 */
030package org.openimaj.image.feature.global;
031
032import org.openimaj.citation.annotation.Reference;
033import org.openimaj.citation.annotation.ReferenceType;
034import org.openimaj.feature.DoubleFV;
035import org.openimaj.feature.FeatureVectorProvider;
036import org.openimaj.image.FImage;
037import org.openimaj.image.MBFImage;
038import org.openimaj.image.analyser.ImageAnalyser;
039import org.openimaj.image.colour.Transforms;
040import org.openimaj.image.pixel.statistics.MaskingHistogramModel;
041import org.openimaj.image.saliency.DepthOfFieldEstimator;
042import org.openimaj.image.saliency.LuoTangSubjectRegion;
043import org.openimaj.math.statistics.distribution.MultidimensionalHistogram;
044
045/**
046 * Estimate the simplicity of an image by looking at the
047 * colour distribution of the background using the algorithm
048 * defined by Yiwen Luo and Xiaoou Tang.
049 * 
050 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
051 */
052@Reference(
053                type = ReferenceType.Inproceedings,
054                author = { "Luo, Yiwen", "Tang, Xiaoou" },
055                title = "Photo and Video Quality Evaluation: Focusing on the Subject",
056                year = "2008",
057                booktitle = "Proceedings of the 10th European Conference on Computer Vision: Part III",
058                pages = { "386", "399" },
059                url = "http://dx.doi.org/10.1007/978-3-540-88690-7_29",
060                publisher = "Springer-Verlag",
061                series = "ECCV '08",
062                customData = { 
063                                "isbn", "978-3-540-88689-1", 
064                                "location", "Marseille, France", 
065                                "numpages", "14", 
066                                "doi", "10.1007/978-3-540-88690-7_29", 
067                                "acmid", "1478204", 
068                                "address", "Berlin, Heidelberg" 
069                }
070)
071public class LuoSimplicity implements ImageAnalyser<MBFImage>, FeatureVectorProvider<DoubleFV> {
072        LuoTangSubjectRegion extractor;
073        int binsPerBand = 16;
074        float gamma = 0.01f;
075        double simplicity;
076        
077        /**
078         * Construct with the defaults of 16 histograms per image band
079         * and a gamma value of 0.01. The defaults are used for the
080         * {@link LuoTangSubjectRegion} extractor.
081         */
082        public LuoSimplicity() {
083                extractor = new LuoTangSubjectRegion();
084        }
085        
086        /**
087         * Construct with the given parameters.
088         * @param binsPerBand the number of histogram bins per colour band
089         * @param gamma the gamma value for determining the threshold
090         * @param alpha the alpha value.
091         * @param maxKernelSize Maximum kernel size for the {@link DepthOfFieldEstimator}.
092         * @param kernelSizeStep Kernel step size for the {@link DepthOfFieldEstimator}.
093         * @param nbins Number of bins for the {@link DepthOfFieldEstimator}.
094         * @param windowSize window size for the {@link DepthOfFieldEstimator}.
095         */
096        public LuoSimplicity(int binsPerBand, float gamma, float alpha, int maxKernelSize, int kernelSizeStep, int nbins, int windowSize) {
097                extractor = new LuoTangSubjectRegion(alpha, maxKernelSize, kernelSizeStep, nbins, windowSize);
098                this.binsPerBand = binsPerBand;
099                this.gamma = gamma;
100        }
101        
102        @Override
103        public void analyseImage(MBFImage image) {
104                Transforms.calculateIntensityNTSC(image).analyseWith(extractor);
105                final FImage mask = extractor.getROIMap().inverse();
106                
107                final MaskingHistogramModel hm = new MaskingHistogramModel(mask, binsPerBand, binsPerBand, binsPerBand);
108                hm.estimateModel(image);
109                
110                final MultidimensionalHistogram fv = hm.getFeatureVector();
111                final double thresh = gamma* fv.max();
112                int count = 0;
113                for (final double f : fv.values) {
114                        if (f >= thresh) 
115                                count++;
116                }
117                
118                simplicity = (double)count / (double)fv.values.length;
119        }
120        
121        @Override
122        public DoubleFV getFeatureVector() {
123                return new DoubleFV(new double[] { simplicity });
124        }
125}