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Methods for the extraction of local features. Local features are descriptions of regions of images (SIFT, ...) selected by detectors (Difference of Gaussian, Harris, ...).
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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
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
package org.openimaj.feature.local.matcher;

import java.util.ArrayList;
import java.util.List;

import org.openimaj.citation.annotation.Reference;
import org.openimaj.citation.annotation.ReferenceType;
import org.openimaj.citation.annotation.References;
import org.openimaj.feature.DoubleFVComparison;
import org.openimaj.feature.local.LocalFeature;
import org.openimaj.util.pair.Pair;

/**
 * Basic local feature matcher. Matches interest points by finding closest two
 * interest points to target and checking whether the distance between the two
 * matches is sufficiently large.
 * 
 * @author Jonathon Hare ([email protected])
 * @param 
 */
@References(references = {
		@Reference(
				type = ReferenceType.Article,
				author = { "David Lowe" },
				title = "Distinctive image features from scale-invariant keypoints",
				year = "2004",
				journal = "IJCV",
				pages = { "91", "110" },
				month = "January",
				number = "2",
				volume = "60"),
		@Reference(
				type = ReferenceType.Inproceedings,
				author = { "David Lowe" },
				title = "Object recognition from local scale-invariant features",
				year = "1999",
				booktitle = "Proc. of the International Conference on Computer Vision {ICCV}",
				pages = { "1150", "1157" }
		)
})
public class BasicMatcher> implements LocalFeatureMatcher {
	protected List modelKeypoints;
	protected List> matches;
	protected int thresh = 8;

	/**
	 * Initialise the matcher setting the threshold which the difference between
	 * the scores of the top two best matches must differ in order to count the
	 * first as a good match.
	 * 
	 * @param threshold
	 */
	public BasicMatcher(int threshold)
	{
		matches = new ArrayList>();
		thresh = threshold;
	}

	/**
	 * @return List of pairs of matching keypoints
	 */
	@Override
	public List> getMatches() {
		return matches;
	}

	@Override
	public boolean findMatches(List keys1)
	{
		matches = new ArrayList>();

		/*
		 * Match the keys in list keys1 to their best matches in keys2.
		 */
		for (final T k : keys1) {
			final T match = checkForMatch(k, modelKeypoints);

			if (match != null) {
				matches.add(new Pair(k, match));
			}
		}

		return true;
	}

	/**
	 * This searches through the keypoints in klist for the two closest matches
	 * to key. If the closest is less than threshold times distance
	 * to second closest, then return the closest match. Otherwise, return NULL.
	 */
	protected T checkForMatch(T query, List features)
	{
		double distsq1 = Double.MAX_VALUE, distsq2 = Double.MAX_VALUE;
		T minkey = null;

		// find two closest matches
		for (final T target : features) {
			final double dsq = target.getFeatureVector().asDoubleFV()
					.compare(query.getFeatureVector().asDoubleFV(), DoubleFVComparison.EUCLIDEAN);

			if (dsq < distsq1) {
				distsq2 = distsq1;
				distsq1 = dsq;
				minkey = target;
			} else if (dsq < distsq2) {
				distsq2 = dsq;
			}
		}

		// check the distance against the threshold
		if (10 * 10 * distsq1 < thresh * thresh * distsq2) {
			return minkey;
		}
		else
			return null;
	}

	@Override
	public void setModelFeatures(List modelkeys) {
		modelKeypoints = modelkeys;
	}

	/**
	 * Set the matching threshold
	 * 
	 * @param thresh
	 *            the threshold
	 */
	public void setThreshold(int thresh) {
		this.thresh = thresh;
	}
}