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 colour distribution of
047 * the background using the algorithm defined by Yiwen Luo and Xiaoou Tang.
048 *
049 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
050 */
051@Reference(
052                type = ReferenceType.Inproceedings,
053                author = { "Luo, Yiwen", "Tang, Xiaoou" },
054                title = "Photo and Video Quality Evaluation: Focusing on the Subject",
055                year = "2008",
056                booktitle = "Proceedings of the 10th European Conference on Computer Vision: Part III",
057                pages = { "386", "399" },
058                url = "http://dx.doi.org/10.1007/978-3-540-88690-7_29",
059                publisher = "Springer-Verlag",
060                series = "ECCV '08",
061                customData = {
062                                "isbn", "978-3-540-88689-1",
063                                "location", "Marseille, France",
064                                "numpages", "14",
065                                "doi", "10.1007/978-3-540-88690-7_29",
066                                "acmid", "1478204",
067                                "address", "Berlin, Heidelberg"
068                })
069public class LuoSimplicity implements ImageAnalyser<MBFImage>, FeatureVectorProvider<DoubleFV> {
070        LuoTangSubjectRegion extractor;
071        int binsPerBand = 16;
072        float gamma = 0.01f;
073        double simplicity;
074
075        /**
076         * Construct with the defaults of 16 histograms per image band and a gamma
077         * value of 0.01. The defaults are used for the {@link LuoTangSubjectRegion}
078         * extractor.
079         */
080        public LuoSimplicity() {
081                extractor = new LuoTangSubjectRegion();
082        }
083
084        /**
085         * Construct with the given parameters.
086         * 
087         * @param binsPerBand
088         *            the number of histogram bins per colour band
089         * @param gamma
090         *            the gamma value for determining the threshold
091         * @param alpha
092         *            the alpha value.
093         * @param maxKernelSize
094         *            Maximum kernel size for the {@link DepthOfFieldEstimator}.
095         * @param kernelSizeStep
096         *            Kernel step size for the {@link DepthOfFieldEstimator}.
097         * @param nbins
098         *            Number of bins for the {@link DepthOfFieldEstimator}.
099         * @param windowSize
100         *            window size for the {@link DepthOfFieldEstimator}.
101         */
102        public LuoSimplicity(int binsPerBand, float gamma, float alpha, int maxKernelSize, int kernelSizeStep, int nbins,
103                        int windowSize)
104        {
105                extractor = new LuoTangSubjectRegion(alpha, maxKernelSize, kernelSizeStep, nbins, windowSize);
106                this.binsPerBand = binsPerBand;
107                this.gamma = gamma;
108        }
109
110        @Override
111        public void analyseImage(MBFImage image) {
112                Transforms.calculateIntensityNTSC(image).analyseWith(extractor);
113                final FImage mask = extractor.getROIMap().inverse();
114
115                final MaskingHistogramModel hm = new MaskingHistogramModel(mask, binsPerBand, binsPerBand, binsPerBand);
116                hm.estimateModel(image);
117
118                final MultidimensionalHistogram fv = hm.getFeatureVector();
119                final double thresh = gamma * fv.max();
120                int count = 0;
121                for (final double f : fv.values) {
122                        if (f >= thresh)
123                                count++;
124                }
125
126                simplicity = (double) count / (double) fv.values.length;
127        }
128
129        @Override
130        public DoubleFV getFeatureVector() {
131                return new DoubleFV(new double[] { simplicity });
132        }
133}