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.model;
031
032import java.io.Serializable;
033
034import org.openimaj.image.FImage;
035import org.openimaj.image.Image;
036import org.openimaj.image.MBFImage;
037import org.openimaj.math.model.EstimatableModel;
038
039/**
040 * An ImageClassificationModel is a {@link EstimatableModel} constructed between
041 * an generic image and a probability map in the form of an FImage.
042 *
043 * Potential uses for such a model are for the prediction of certain classes of
044 * pixels in an image. For example, a model could be constructed that predicted
045 * skin-tones in an image based on hue and saturation values of pixels. With
046 * such a model, a colour image could be presented, and a probability map would
047 * be returned.
048 *
049 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
050 * @param <T>
051 *            the type of image that the model can be applied to
052 *
053 */
054public interface ImageClassificationModel<T extends Image<?, T>> extends EstimatableModel<T, FImage>, Serializable {
055        /**
056         * Learn the model from the given {@link MBFImage}s.
057         *
058         * @param images
059         *            the images to learn from
060         */
061        public abstract void learnModel(@SuppressWarnings("unchecked") T... images);
062
063        /**
064         * Classify the given image and return the corresponding probability map
065         *
066         * @param im
067         *            the image to classify
068         * @return the probability map
069         */
070        public abstract FImage classifyImage(T im);
071
072        @Override
073        public abstract ImageClassificationModel<T> clone();
074}