Example Scene Classification

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Scene classification is the problem where you are presented with an image and you need to classify it as belonging to a known set. The example below demonstrates how to perform scene classification using dense SURF features and a nearest neighbour classifier. See code documentation for a more detailed discussion.

Example Code:

Concepts:

  • Scene Classification
  • Dense Image Features
  • Clustering
  • k-NN classifier

Related Examples:

Example Code

/**
 * <p>
 * Example of how to train a K-NN bow-of-word classifier for scene recognition.  The resulting classifier
 * produces results which are correct 52.2% of the time.  To provide a point of comparison, randomly selecting
 * a scene is about 6.7% accurate, SVM One vs One RBF classifier can produce accuracy of around 74% and
 * other people using different techniques claim to have achieved around 85% accurate with more advanced
 * techniques.
 * </p>
 *
 * Training Steps:
 * <ol>
 * <li>Compute dense SURF features across the training data set.</li>
 * <li>Cluster using k-means to create works.</li>
 * <li>For each image compute the histogram of words found in the image</li>
 * <li>Save word histograms and image scene labels in a classifier</li>
 * </ol>
 *
 * Testing Steps:
 * <ol>
 * <li>For each image in the testing data set compute its histogram</li>
 * <li>Look up the k-nearest-neighbors for that histogram</li>
 * <li>Classify an image by by selecting the scene type with the most neighbors</li>
 * </ol>
 *
 * <p>NOTE: Scene recognition is still very much a work in progress in BoofCV and the code is likely to be
 * significantly modified in the future.</p>
 *
 * @author Peter Abeles
 */
public class ExampleClassifySceneKnn extends LearnSceneFromFiles {
 
	// Tuning parameters
	public static int NUMBER_OF_WORDS = 100;
	public static boolean HISTOGRAM_HARD = true;
	public static int NUM_NEIGHBORS = 10;
	public static int MAX_KNN_ITERATIONS = 100;
 
	// Files intermediate results are stored in
	public static final String CLUSTER_FILE_NAME = "clusters.obj";
	public static final String HISTOGRAM_FILE_NAME = "histograms.obj";
 
	// Algorithms
	ClusterVisualWords cluster;
	DescribeImageDense<GrayU8,TupleDesc_F64> describeImage;
	NearestNeighbor<HistogramScene> nn;
 
	ClassifierKNearestNeighborsBow<GrayU8,TupleDesc_F64> classifier;
 
	public ExampleClassifySceneKnn(final DescribeImageDense<GrayU8, TupleDesc_F64> describeImage,
								   ComputeClusters<double[]> clusterer,
								   NearestNeighbor<HistogramScene> nn) {
		this.describeImage = describeImage;
		this.cluster = new ClusterVisualWords(clusterer, describeImage.createDescription().size(),0xFEEDBEEF);
		this.nn = nn;
	}
 
	/**
	 * Process all the data in the training data set to learn the classifications.  See code for details.
	 */
	public void learnAndSave() {
		System.out.println("======== Learning Classifier");
 
		// Either load pre-computed words or compute the words from the training images
		AssignCluster<double[]> assignment;
		if( new File(CLUSTER_FILE_NAME).exists() ) {
			assignment = UtilIO.load(CLUSTER_FILE_NAME);
		} else {
			System.out.println(" Computing clusters");
			assignment = computeClusters();
		}
 
		// Use these clusters to assign features to words
		FeatureToWordHistogram_F64 featuresToHistogram = new FeatureToWordHistogram_F64(assignment,HISTOGRAM_HARD);
 
		// Storage for the work histogram in each image in the training set and their label
		List<HistogramScene> memory;
 
		if( !new File(HISTOGRAM_FILE_NAME).exists() ) {
			System.out.println(" computing histograms");
			memory = computeHistograms(featuresToHistogram);
			UtilIO.save(memory,HISTOGRAM_FILE_NAME);
		}
	}
 
	/**
	 * Extract dense features across the training set.  Then clusters are found within those features.
	 */
	private AssignCluster<double[]> computeClusters() {
		System.out.println("Image Features");
 
		// computes features in the training image set
		List<TupleDesc_F64> features = new ArrayList<>();
		for( String scene : train.keySet() ) {
			List<String> imagePaths = train.get(scene);
			System.out.println("   " + scene);
 
			for( String path : imagePaths ) {
				GrayU8 image = UtilImageIO.loadImage(path, GrayU8.class);
				describeImage.process(image);
 
				// the descriptions will get recycled on the next call, so create a copy
				for( TupleDesc_F64 d : describeImage.getDescriptions() ) {
					features.add( d.copy() );
				}
			}
		}
		// add the features to the overall list which the clusters will be found inside of
		for (int i = 0; i < features.size(); i++) {
			cluster.addReference(features.get(i));
		}
 
		System.out.println("Clustering");
		// Find the clusters.  This can take a bit
		cluster.process(NUMBER_OF_WORDS);
 
		UtilIO.save(cluster.getAssignment(), CLUSTER_FILE_NAME);
 
		return cluster.getAssignment();
	}
 
	public void loadAndCreateClassifier() {
		// load results from a file
		List<HistogramScene> memory = UtilIO.load(HISTOGRAM_FILE_NAME);
		AssignCluster<double[]> assignment = UtilIO.load(CLUSTER_FILE_NAME);
 
		FeatureToWordHistogram_F64 featuresToHistogram = new FeatureToWordHistogram_F64(assignment,HISTOGRAM_HARD);
 
 
		// Provide the training results to K-NN and it will preprocess these results for quick lookup later on
		// Can use this classifier with saved results and avoid the
 
		classifier = new ClassifierKNearestNeighborsBow<>(nn, describeImage, featuresToHistogram);
		classifier.setClassificationData(memory, getScenes().size());
		classifier.setNumNeighbors(NUM_NEIGHBORS);
	}
 
	/**
	 * For all the images in the training data set it computes a {@link HistogramScene}.  That data structure
	 * contains the word histogram and the scene that the histogram belongs to.
	 */
	private List<HistogramScene> computeHistograms(FeatureToWordHistogram_F64 featuresToHistogram ) {
 
		List<String> scenes = getScenes();
 
		List<HistogramScene> memory;// Processed results which will be passed into the k-NN algorithm
		memory = new ArrayList<>();
 
		for( int sceneIndex = 0; sceneIndex < scenes.size(); sceneIndex++ ) {
			String scene = scenes.get(sceneIndex);
			System.out.println("   " + scene);
			List<String> imagePaths = train.get(scene);
 
			for (String path : imagePaths) {
				GrayU8 image = UtilImageIO.loadImage(path, GrayU8.class);
 
				// reset before processing a new image
				featuresToHistogram.reset();
				describeImage.process(image);
				for ( TupleDesc_F64 d : describeImage.getDescriptions() ) {
					featuresToHistogram.addFeature(d);
				}
				featuresToHistogram.process();
 
				// The histogram is already normalized so that it sums up to 1.  This provides invariance
				// against the overall number of features changing.
				double[] histogram = featuresToHistogram.getHistogram();
 
				// Create the data structure used by the KNN classifier
				HistogramScene imageHist = new HistogramScene(NUMBER_OF_WORDS);
				imageHist.setHistogram(histogram);
				imageHist.type = sceneIndex;
 
				memory.add(imageHist);
			}
		}
		return memory;
	}
 
	@Override
	protected int classify(String path) {
		GrayU8 image = UtilImageIO.loadImage(path, GrayU8.class);
 
		return classifier.classify(image);
	}
 
	public static void main(String[] args) {
 
		ConfigDenseSurfFast surfFast = new ConfigDenseSurfFast(new DenseSampling(8,8));
		ConfigDenseSurfStable surfStable = new ConfigDenseSurfStable(new DenseSampling(8,8));
		ConfigDenseSift sift = new ConfigDenseSift(new DenseSampling(6,6));
		ConfigDenseHoG hog = new ConfigDenseHoG();
 
		DescribeImageDense<GrayU8,TupleDesc_F64> desc = (DescribeImageDense)
				FactoryDescribeImageDense.surfFast(surfFast, GrayU8.class);
//				FactoryDescribeImageDense.surfStable(surfStable, GrayU8.class);
//				FactoryDescribeImageDense.sift(sift, GrayU8.class);
//				FactoryDescribeImageDense.hog(hog, ImageType.single(GrayU8.class));
 
		ComputeClusters<double[]> clusterer = FactoryClustering.kMeans_F64(null, MAX_KNN_ITERATIONS, 20, 1e-6);
		clusterer.setVerbose(true);
 
		NearestNeighbor<HistogramScene> nn = FactoryNearestNeighbor.exhaustive();
		ExampleClassifySceneKnn example = new ExampleClassifySceneKnn(desc,clusterer,nn);
 
		File trainingDir = new File(UtilIO.pathExample("learning/scene/train"));
		File testingDir = new File(UtilIO.pathExample("learning/scene/test"));
 
		if( !trainingDir.exists() || !testingDir.exists() ) {
			String addressSrc = "http://jaist.dl.sourceforge.net/project/boofcv/datasets/bow_data_v001.zip";
			File dst = trainingDir.getParentFile();
			DeepBoofDataBaseOps.download(addressSrc,dst);
			DeepBoofDataBaseOps.decompressZip(new File(dst, "bow_data_v001.zip"), dst, true);
			System.out.println("Download complete!");
		} else {
			System.out.println("Delete and download again if there are file not found errors");
			System.out.println("   "+trainingDir);
			System.out.println("   "+testingDir);
		}
 
		example.loadSets(trainingDir, null, testingDir);
		// train the classifier
		example.learnAndSave();
		// now load it for evaluation purposes from the files
		example.loadAndCreateClassifier();
 
		// test the classifier on the test set
		Confusion confusion = example.evaluateTest();
		confusion.getMatrix().print();
		System.out.println("Accuracy = " + confusion.computeAccuracy());
 
		// Show confusion matrix
		// Not the best coloration scheme...  perfect = red diagonal and blue elsewhere.
		ShowImages.showWindow(new ConfusionMatrixPanel(
				confusion.getMatrix(),example.getScenes(), 400, true), "Confusion Matrix", true);
 
		// For SIFT descriptor the accuracy is          54.0%
		// For  "fast"  SURF descriptor the accuracy is 52.2%
		// For "stable" SURF descriptor the accuracy is 49.4%
		// For HOG                                      53.3%
 
		// SURF results are interesting. "Stable" is significantly better than "fast"!
		// One explanation is that the descriptor for "fast" samples a smaller region than "stable", by a
		// couple of pixels at scale of 1.  Thus there is less overlap between the features.
 
		// Reducing the size of "stable" to 0.95 does slightly improve performance to 50.5%, can't scale it down
		// much more without performance going down
	}
}