org.openimaj.image.feature.global.ModifiedLuoSimplicity Maven / Gradle / Ivy
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/**
* Copyright (c) 2011, The University of Southampton and the individual contributors.
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification,
* are permitted provided that the following conditions are met:
*
* * Redistributions of source code must retain the above copyright notice,
* this list of conditions and the following disclaimer.
*
* * Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* * Neither the name of the University of Southampton nor the names of its
* contributors may be used to endorse or promote products derived from this
* software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
* ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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package org.openimaj.image.feature.global;
import gnu.trove.map.hash.TObjectFloatHashMap;
import gnu.trove.procedure.TObjectFloatProcedure;
import org.openimaj.citation.annotation.Reference;
import org.openimaj.citation.annotation.ReferenceType;
import org.openimaj.citation.annotation.References;
import org.openimaj.feature.DoubleFV;
import org.openimaj.feature.FeatureVectorProvider;
import org.openimaj.image.FImage;
import org.openimaj.image.MBFImage;
import org.openimaj.image.analyser.ImageAnalyser;
import org.openimaj.image.pixel.ConnectedComponent;
import org.openimaj.image.pixel.statistics.MaskingHistogramModel;
import org.openimaj.image.processor.connectedcomponent.render.BoundingBoxRenderer;
import org.openimaj.image.saliency.AchantaSaliency;
import org.openimaj.image.saliency.YehSaliency;
import org.openimaj.image.segmentation.FelzenszwalbHuttenlocherSegmenter;
import org.openimaj.math.statistics.distribution.MultidimensionalHistogram;
import org.openimaj.util.array.ArrayUtils;
/**
* Estimate the simplicity of an image by looking at the colour distribution of
* the background.
*
* Algorithm based on that proposed by Yiwen Luo and Xiaoou Tang, but modified
* to use the foreground detection approach suggested in Che-Hua Yeh et al.
*
* @author Jonathon Hare ([email protected])
*/
@References(references = {
@Reference(
type = ReferenceType.Inproceedings,
author = { "Luo, Yiwen", "Tang, Xiaoou" },
title = "Photo and Video Quality Evaluation: Focusing on the Subject",
year = "2008",
booktitle = "Proceedings of the 10th European Conference on Computer Vision: Part III",
pages = { "386", "399" },
url = "http://dx.doi.org/10.1007/978-3-540-88690-7_29",
publisher = "Springer-Verlag",
series = "ECCV '08",
customData = { "isbn", "978-3-540-88689-1", "location", "Marseille, France", "numpages", "14", "doi",
"10.1007/978-3-540-88690-7_29", "acmid", "1478204", "address", "Berlin, Heidelberg" }),
@Reference(
type = ReferenceType.Inproceedings,
author = { "Che-Hua Yeh", "Yuan-Chen Ho", "Brian A. Barsky", "Ming Ouhyoung" },
title = "Personalized Photograph Ranking and Selection System",
year = "2010",
booktitle = "Proceedings of ACM Multimedia",
pages = { "211", "220" },
month = "October",
customData = { "location", "Florence, Italy" }) })
public class ModifiedLuoSimplicity implements ImageAnalyser, FeatureVectorProvider {
protected YehSaliency extractor;
protected float alpha = 0.67f;
protected int binsPerBand = 16;
protected float gamma = 0.01f;
protected boolean boxMode = true;
protected double simplicity;
/**
* Construct with the default values
*/
public ModifiedLuoSimplicity() {
extractor = new YehSaliency();
}
/**
* Construct with the given values
*
* @param binsPerBand
* the number of histogram bins per colour band
* @param gamma
* the gamma value for determining the threshold
* @param boxMode
* whether to extract rectangular boxes for the foreground
* regions (true) or to just use the pixels (false)
* @param alpha
* the alpha value for determining the foreground/background
* threshold
* @param saliencySigma
* smoothing for the {@link AchantaSaliency} class
* @param segmenterSigma
* smoothing for {@link FelzenszwalbHuttenlocherSegmenter}.
* @param k
* k value for {@link FelzenszwalbHuttenlocherSegmenter}.
* @param minSize
* minimum region size for
* {@link FelzenszwalbHuttenlocherSegmenter}.
*/
public ModifiedLuoSimplicity(int binsPerBand, float gamma, boolean boxMode, float alpha, float saliencySigma,
float segmenterSigma, float k, int minSize)
{
extractor = new YehSaliency(saliencySigma, segmenterSigma, k, minSize);
this.binsPerBand = binsPerBand;
this.gamma = gamma;
this.boxMode = boxMode;
this.alpha = alpha;
}
/*
* (non-Javadoc)
*
* @see
* org.openimaj.image.analyser.ImageAnalyser#analyseImage(org.openimaj.image
* .Image)
*/
@Override
public void analyseImage(MBFImage image) {
image.analyseWith(extractor);
FImage mask;
if (boxMode) {
final TObjectFloatHashMap componentMap = extractor.getSaliencyComponents();
final float max = ArrayUtils.maxValue(componentMap.values());
mask = new FImage(image.getWidth(), image.getHeight());
final float thresh = max * alpha;
final BoundingBoxRenderer renderer = new BoundingBoxRenderer(mask, 1F, true);
componentMap.forEachEntry(new TObjectFloatProcedure() {
@Override
public boolean execute(ConnectedComponent cc, float sal) {
if (sal >= thresh) { // note that this is reversed from the
// paper, which doesn't seem to make
// sense.
renderer.process(cc);
}
return true;
}
});
} else {
mask = extractor.getSaliencyMap();
final float maskthresh = mask.max() * alpha;
mask = mask.threshold(maskthresh);
}
mask = mask.inverse();
final MaskingHistogramModel hm = new MaskingHistogramModel(mask, binsPerBand, binsPerBand, binsPerBand);
hm.estimateModel(image);
final MultidimensionalHistogram fv = hm.getFeatureVector();
final double thresh = gamma * fv.max();
int count = 0;
for (final double f : fv.values) {
if (f >= thresh)
count++;
}
simplicity = (double) count / (double) fv.values.length;
}
@Override
public DoubleFV getFeatureVector() {
return new DoubleFV(new double[] { simplicity });
}
}