1 /**
2 * Copyright (c) 2011, The University of Southampton and the individual contributors.
3 * All rights reserved.
4 *
5 * Redistribution and use in source and binary forms, with or without modification,
6 * are permitted provided that the following conditions are met:
7 *
8 * * Redistributions of source code must retain the above copyright notice,
9 * this list of conditions and the following disclaimer.
10 *
11 * * Redistributions in binary form must reproduce the above copyright notice,
12 * this list of conditions and the following disclaimer in the documentation
13 * and/or other materials provided with the distribution.
14 *
15 * * Neither the name of the University of Southampton nor the names of its
16 * contributors may be used to endorse or promote products derived from this
17 * software without specific prior written permission.
18 *
19 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
20 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
21 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
22 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
23 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
24 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
25 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
26 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
27 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
28 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
29 */
30 package org.openimaj.image.model;
31
32 import java.io.Serializable;
33
34 import org.openimaj.image.FImage;
35 import org.openimaj.image.Image;
36 import org.openimaj.image.MBFImage;
37 import org.openimaj.math.model.EstimatableModel;
38
39 /**
40 * An ImageClassificationModel is a {@link EstimatableModel} constructed between
41 * an generic image and a probability map in the form of an FImage.
42 *
43 * Potential uses for such a model are for the prediction of certain classes of
44 * pixels in an image. For example, a model could be constructed that predicted
45 * skin-tones in an image based on hue and saturation values of pixels. With
46 * such a model, a colour image could be presented, and a probability map would
47 * be returned.
48 *
49 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
50 * @param <T>
51 * the type of image that the model can be applied to
52 *
53 */
54 public interface ImageClassificationModel<T extends Image<?, T>> extends EstimatableModel<T, FImage>, Serializable {
55 /**
56 * Learn the model from the given {@link MBFImage}s.
57 *
58 * @param images
59 * the images to learn from
60 */
61 public abstract void learnModel(@SuppressWarnings("unchecked") T... images);
62
63 /**
64 * Classify the given image and return the corresponding probability map
65 *
66 * @param im
67 * the image to classify
68 * @return the probability map
69 */
70 public abstract FImage classifyImage(T im);
71
72 @Override
73 public abstract ImageClassificationModel<T> clone();
74 }