Lucene image retrieval是以圖搜圖的java開源框架，這幾天沒什麼事，就讀了點源碼，並寫了點註釋，特在這分享給大家。
這裏主要給出的是BOVWBuilder.java、Kmeans.java及Cluster.java。就是用詞頻對特徵進行編碼，用到是BOF(bag of feature)模型，原理就是提取N張圖片的特徵(比如sift),放在一起就可以得到矩陣，然後對矩陣進行kmeans聚類，就會到到若干個聚類中心；對於新來的一副圖像，我們分別計算該特徵點與那個聚類中心最近，這樣該聚類中心的量值就加1，這樣就可以編碼得到與聚類中心個數想等的維數向量。
一切都從BOVWBuilder中index函數開始...
BOVWBuilder.java(包含註釋)
package lmc.imageretrieval.imageanalysis.bovw;
import java.io.File;
import java.io.IOException;
import java.text.DecimalFormat;
import java.text.NumberFormat;
import java.util.Arrays;
import java.util.HashSet;
import java.util.Iterator;
import java.util.LinkedList;
import javax.swing.ProgressMonitor;
import lmc.imageretrieval.imageanalysis.Histogram;
import lmc.imageretrieval.imageanalysis.LireFeature;
import lmc.imageretrieval.tools.DocumentBuilder;
import lmc.imageretrieval.utils.SerializationUtils;
import org.apache.lucene.analysis.core.WhitespaceAnalyzer;
import org.apache.lucene.document.Document;
import org.apache.lucene.document.Field;
import org.apache.lucene.document.StoredField;
import org.apache.lucene.document.TextField;
import org.apache.lucene.index.DirectoryReader;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.IndexWriter;
import org.apache.lucene.index.IndexWriterConfig;
import org.apache.lucene.index.IndexWriterConfig.OpenMode;
import org.apache.lucene.index.IndexableField;
import org.apache.lucene.index.MultiFields;
import org.apache.lucene.index.Term;
import org.apache.lucene.util.Bits;
import org.apache.lucene.util.Version;
public class BOVWBuilder {
IndexReader reader;
// number of documents used to build the vocabulary / clusters.
private int numDocsForVocabulary = 500;
private int numClusters = 512;
private Cluster[] clusters = null;
DecimalFormat df = (DecimalFormat) NumberFormat.getNumberInstance();
private ProgressMonitor pm = null;
protected LireFeature lireFeature;
protected String localFeatureFieldName;
protected String visualWordsFieldName;
protected String localFeatureHistFieldName;
protected String clusterFile;
public static boolean DELETE_LOCAL_FEATURES = true;
/**
*
* @param reader
* @deprecated
*/
public BOVWBuilder(IndexReader reader) {
this.reader = reader;
}
/**
* Creates a new instance of the BOVWBuilder using the given reader. The numDocsForVocabulary
* indicates how many documents of the index are used to build the vocabulary (clusters).
*
* @param reader the reader used to open the Lucene index,
* @param numDocsForVocabulary gives the number of documents for building the vocabulary (clusters).
* @deprecated
*/
public BOVWBuilder(IndexReader reader, int numDocsForVocabulary) {
this.reader = reader;
this.numDocsForVocabulary = numDocsForVocabulary;
}
/**
* Creates a new instance of the BOVWBuilder using the given reader. The numDocsForVocabulary
* indicates how many documents of the index are used to build the vocabulary (clusters). The numClusters gives
* the number of clusters k-means should find. Note that this number should be lower than the number of features,
* otherwise an exception will be thrown while indexing.
*
* @param reader the index reader
* @param numDocsForVocabulary the number of documents that should be sampled for building the visual vocabulary
* @param numClusters the size of the visual vocabulary
* @deprecated
*/
public BOVWBuilder(IndexReader reader, int numDocsForVocabulary, int numClusters) {
this.numDocsForVocabulary = numDocsForVocabulary;
this.numClusters = numClusters;
this.reader = reader;
}
/**
* Creates a new instance of the BOVWBuilder using the given reader. TODO: write
*
* @param reader the index reader
* @param lireFeature lireFeature used
*/
public BOVWBuilder(IndexReader reader, LireFeature lireFeature) {
this.reader = reader;
this.lireFeature = lireFeature;
}
/**
* Creates a new instance of the BOVWBuilder using the given reader. The numDocsForVocabulary
* indicates how many documents of the index are used to build the vocabulary (clusters).
* TODO: write
*
* @param reader the index reader
* @param lireFeature lireFeature used
* @param numDocsForVocabulary the number of documents that should be sampled for building the visual vocabulary
*/
public BOVWBuilder(IndexReader reader, LireFeature lireFeature, int numDocsForVocabulary) {
this.numDocsForVocabulary = numDocsForVocabulary;
this.reader = reader;
this.lireFeature = lireFeature;
}
/**
* Creates a new instance of the BOVWBuilder using the given reader. The numDocsForVocabulary
* indicates how many documents of the index are used to build the vocabulary (clusters). The numClusters gives
* the number of clusters k-means should find. Note that this number should be lower than the number of features,
* otherwise an exception will be thrown while indexing. TODO: write
*
* @param reader the index reader
* @param lireFeature lireFeature used
* @param numDocsForVocabulary the number of documents that should be sampled for building the visual vocabulary
* @param numClusters the size of the visual vocabulary
*/
public BOVWBuilder(IndexReader reader, LireFeature lireFeature, int numDocsForVocabulary, int numClusters) {
this.numDocsForVocabulary = numDocsForVocabulary;
this.numClusters = numClusters;
this.reader = reader;
this.lireFeature = lireFeature;
}
protected void init() {
localFeatureFieldName = lireFeature.getFieldName();
visualWordsFieldName = lireFeature.getFieldName() + DocumentBuilder.FIELD_NAME_BOVW;
localFeatureHistFieldName = lireFeature.getFieldName()+ DocumentBuilder.FIELD_NAME_BOVW_VECTOR;
clusterFile = "./clusters-bovw" + lireFeature.getFeatureName() + ".dat";
}
/**
* Uses an existing index, where each and every document should have a set of local features. A number of
* random images (numDocsForVocabulary) is selected and clustered to get a vocabulary of visual words
* (the cluster means). For all images a histogram on the visual words is created and added to the documents.
* Pre-existing histograms are deleted, so this method can be used for re-indexing.
*
* @throws java.io.IOException
*/
public void index() throws IOException {
init();
df.setMaximumFractionDigits(3);
// find the documents for building the vocabulary:
HashSet<Integer> docIDs = selectVocabularyDocs(); //選擇全部要進行聚類的文檔docment的id
KMeans k = new KMeans(numClusters);
// fill the KMeans object:
LinkedList<double[]> features = new LinkedList<double[]>();
// Needed for check whether the document is deleted.
Bits liveDocs = MultiFields.getLiveDocs(reader);
for (Iterator<Integer> iterator = docIDs.iterator(); iterator.hasNext(); ) {
int nextDoc = iterator.next();
if (reader.hasDeletions() && !liveDocs.get(nextDoc)) continue; // if it is deleted, just ignore it.
Document d = reader.document(nextDoc); // 取出該文檔
features.clear();
IndexableField[] fields = d.getFields(localFeatureFieldName); // 取出sift特徵點
String file = d.getValues(DocumentBuilder.FIELD_NAME_IDENTIFIER)[0]; // 取出該圖片路徑名字
for (int j = 0; j < fields.length; j++) {
LireFeature f = getFeatureInstance();
// 取出descriptor
f.setByteArrayRepresentation(fields[j].binaryValue().bytes, fields[j].binaryValue().offset, fields[j].binaryValue().length);
// copy the data over to new array ... 沒有用
//double[] feat = new double[f.getDoubleHistogram().length];
//System.arraycopy(f.getDoubleHistogram(), 0, feat, 0, feat.length);
features.add(f.getDoubleHistogram());
}
k.addImage(file, features); // 將descriptor與圖片相關聯
}
if (pm != null) { // set to 5 of 100 before clustering starts.
pm.setProgress(5);
pm.setNote("Starting clustering");
}
if (k.getFeatureCount() < numClusters) { // 總的特徵數小於聚類中心個數，則拋出異常
// this cannot work. You need more data points than clusters.
throw new UnsupportedOperationException("Only " + features.size() + " features found to cluster in " + numClusters + ". Try to use less clusters or more images.");
}
// do the clustering:
System.out.println("Number of local features: " + df.format(k.getFeatureCount()));
System.out.println("Starting clustering ...");
k.init(); // 聚類中心初始化
System.out.println("Step.");
double time = System.currentTimeMillis();
double laststress = k.clusteringStep(); // 進行聚類，並獲得sum of squared error
if (pm != null) { // set to 8 of 100 after first step.
pm.setProgress(8);
pm.setNote("Step 1 finished");
}
System.out.println(getDuration(time) + " -> Next step.");
time = System.currentTimeMillis();
double newStress = k.clusteringStep(); // 第二步聚類
if (pm != null) { // set to 11 of 100 after second step.
pm.setProgress(11);
pm.setNote("Step 2 finished");
}
// critical part: Give the difference in between steps as a constraint for accuracy vs. runtime trade off.
double threshold = Math.max(20d, (double) k.getFeatureCount() / 1000d); // 如果兩次sse小於20 迭代停止
System.out.println("Threshold = " + df.format(threshold));
int cstep = 3;
while (Math.abs(newStress - laststress) > threshold && cstep < 12) { // 迭代次數超過12次，迭代停止
System.out.println(getDuration(time) + " -> Next step. Stress difference ~ |" + (int) newStress + " - " + (int) laststress + "| = " + df.format(Math.abs(newStress - laststress)));
time = System.currentTimeMillis();
laststress = newStress;
newStress = k.clusteringStep();
if (pm != null) { // set to XX of 100 after second step.
pm.setProgress(cstep * 3 + 5);
pm.setNote("Step " + cstep + " finished");
}
cstep++;
}
// Serializing clusters to a file on the disk ...
clusters = k.getClusters(); // 得到聚類中心
// for (int i = 0; i < clusters.length; i++) {
// Cluster cluster = clusters[i];
// System.out.print(cluster.getMembers().size() + ", ");
// }
// System.out.println();
Cluster.writeClusters(clusters, clusterFile); // 將聚類中心點寫入文本文件
// create & store histograms:
System.out.println("Creating histograms ...");
time = System.currentTimeMillis();
// int[] tmpHist = new int[numClusters];
@SuppressWarnings("deprecation")
IndexWriterConfig conf = new IndexWriterConfig(Version.LUCENE_4_10_2,
new WhitespaceAnalyzer(Version.LUCENE_4_10_2));
conf.setOpenMode(OpenMode.CREATE_OR_APPEND);
IndexWriter iw = new IndexWriter(((DirectoryReader) reader).directory(), conf);
if (pm != null) { // set to 50 of 100 after clustering.
pm.setProgress(50);
pm.setNote("Clustering finished");
}
// parallelized indexing
LinkedList<Thread> threads = new LinkedList<Thread>(); // 線程隊列
int numThreads = 8; // 設置了8個線程
// careful: copy reader to RAM for faster access when reading ...
// reader = IndexReader.open(new RAMDirectory(reader.directory()), true);
int step = reader.maxDoc() / numThreads; // 對每個線程分配一定數量的任務
for (int part = 0; part < numThreads; part++) {
Indexer indexer = null;
if (part < numThreads - 1) indexer = new Indexer(part * step, (part + 1) * step, iw, null);
else indexer = new Indexer(part * step, reader.maxDoc(), iw, pm);
Thread t = new Thread(indexer);
threads.add(t);
t.start();
}
for (Iterator<Thread> iterator = threads.iterator(); iterator.hasNext(); ) {
Thread next = iterator.next();
try {
next.join();
} catch (InterruptedException e) {
e.printStackTrace();
}
}
if (pm != null) { // set to 50 of 100 after clustering.
pm.setProgress(95);
pm.setNote("Indexing finished, optimizing index now.");
}
System.out.println(getDuration(time));
iw.commit();
// this one does the "old" commit(), it removes the deleted SURF features.
iw.forceMerge(1);
iw.close();
if (pm != null) { // set to 50 of 100 after clustering.
pm.setProgress(100);
pm.setNote("Indexing & optimization finished");
pm.close();
}
System.out.println("Finished.");
}
// 此函數沒有用
public void indexMissing() throws IOException {
init();
// Reading clusters from disk:
clusters = Cluster.readClusters(clusterFile);
// create & store histograms:
System.out.println("Creating histograms ...");
LireFeature f = getFeatureInstance();
// Needed for check whether the document is deleted.
Bits liveDocs = MultiFields.getLiveDocs(reader);
// based on bug report from Einav Itamar <[email protected]>
@SuppressWarnings("deprecation")
IndexWriterConfig conf = new IndexWriterConfig(Version.LUCENE_4_10_2,
new WhitespaceAnalyzer(Version.LUCENE_4_10_2));
IndexWriter iw = new IndexWriter(((DirectoryReader) reader).directory(), conf);
for (int i = 0; i < reader.maxDoc(); i++) {
if (reader.hasDeletions() && !liveDocs.get(i)) continue; // if it is deleted, just ignore it.
Document d = reader.document(i);
// Only if there are no values yet:
if (d.getValues(visualWordsFieldName) == null || d.getValues(visualWordsFieldName).length == 0) {
createVisualWords(d, f);
// now write the new one. we use the identifier to update ;)
iw.updateDocument(new Term(DocumentBuilder.FIELD_NAME_IDENTIFIER, d.getValues(DocumentBuilder.FIELD_NAME_IDENTIFIER)[0]), d);
}
}
iw.commit();
// added to permanently remove the deleted docs.
iw.forceMerge(1);
iw.close();
System.out.println("Finished.");
}
/**
* Takes one single document and creates the visual words and adds them to the document. The same document is returned.
*
* @param d the document to use for adding the visual words
* @return
* @throws IOException
*/
public Document getVisualWords(Document d) throws IOException { // 得到文檔d所對應的bow特徵
clusters = Cluster.readClusters(clusterFile); // 讀入聚類中心
LireFeature f = getFeatureInstance();
createVisualWords(d, f); // 創建bow特徵
return d;
}
@SuppressWarnings("unused") // 沒有用了
private void quantize(double[] histogram) {
double max = 0;
for (int i = 0; i < histogram.length; i++) {
max = Math.max(max, histogram[i]);
}
for (int i = 0; i < histogram.length; i++) {
histogram[i] = (int) Math.floor((histogram[i] * 128d) / max);
}
}
/**
* Find the appropriate cluster for a given feature.
*
* @param f
* @return the index of the cluster.
*/
private int clusterForFeature(Histogram f) { // 找到一個特徵點最近的聚類中心並返回該聚類中心的下標
double distance = clusters[0].getDistance(f);
double tmp;
int result = 0;
for (int i = 1; i < clusters.length; i++) {
tmp = clusters[i].getDistance(f);
if (tmp < distance) {
distance = tmp;
result = i;
}
}
return result;
}
private String arrayToVisualWordString(double[] hist) { // 以這種string類型進行存儲，感覺沒什麼用啊
StringBuilder sb = new StringBuilder(1024);
for (int i = 0; i < hist.length; i++) {
int visualWordIndex = (int) hist[i];
for (int j = 0; j < visualWordIndex; j++) {
// sb.append('v');
sb.append(Integer.toHexString(i));
sb.append(' ');
}
}
return sb.toString();
}
// 選擇圖片進行聚類
private HashSet<Integer> selectVocabularyDocs() throws IOException {
// need to make sure that this is not running forever ...
int loopCount = 0;
float maxDocs = reader.maxDoc(); // 返回總文檔數量
int capacity = (int) Math.min(numDocsForVocabulary, maxDocs);
if (capacity < 0) capacity = (int) (maxDocs / 2); // 如果是-1 則選擇一半文檔
HashSet<Integer> result = new HashSet<Integer>(capacity);
int tmpDocNumber, tmpIndex;
LinkedList<Integer> docCandidates = new LinkedList<Integer>();
// three cases:
//
// either it's more or the same number as documents
if (numDocsForVocabulary >= maxDocs) { // 指定數量大於已有的，則將已有全部用來聚類
for (int i = 0; i < maxDocs; i++) {
result.add(i);
}
return result;
} else if (numDocsForVocabulary >= maxDocs - 100) { // 在[maxDocs-100, maxDocs]之間，
for (int i = 0; i < maxDocs; i++) {
result.add(i); // 先全部加入
}
while (result.size() > numDocsForVocabulary) { // 隨機踢出掉多餘的圖片，使數量爲numDocForVocabulary
result.remove((int) Math.floor(Math.random() * result.size()));
}
return result;
} else { // 不滿足上面幾種情況即numDocForVocabulary在[1, maxDocs-100]之間
for (int i = 0; i < maxDocs; i++) {
docCandidates.add(i); // 先將全部加入
}
for (int r = 0; r < capacity; r++) { // capacity就等於numDocForVocabulary
boolean worksFine = false;
do {
tmpIndex = (int) Math.floor(Math.random() * (double) docCandidates.size());
tmpDocNumber = docCandidates.get(tmpIndex);
docCandidates.remove(tmpIndex);
// 該文檔是否存在及是否已經包含
// check if the selected doc number is valid: not null, not deleted and not already chosen.
worksFine = (reader.document(tmpDocNumber) != null) && !result.contains(tmpDocNumber);
} while (!worksFine);
result.add(tmpDocNumber);
// need to make sure that this is not running forever ...
if (loopCount++ > capacity * 100)
throw new UnsupportedOperationException("Could not get the documents, maybe there are not enough documents in the index?");
}
return result;
}
}
// protected abstract LireFeature getFeatureInstance();
protected LireFeature getFeatureInstance() {
LireFeature result = null;
try {
result = lireFeature.getClass().newInstance();
} catch (InstantiationException e) {
e.printStackTrace();
} catch (IllegalAccessException e) {
e.printStackTrace();
}
return result;
}
private class Indexer implements Runnable { // 建索引的線程類 私有的
int start, end;
IndexWriter iw;
ProgressMonitor pm = null;
private Indexer(int start, int end, IndexWriter iw, ProgressMonitor pm) {
this.start = start;
this.end = end;
this.iw = iw;
this.pm = pm;
}
public void run() { // 線程運行函數
LireFeature f = getFeatureInstance(); // 得到feature的實例
for (int i = start; i < end; i++) {
try {
Document d = reader.document(i); // 得到第i個文檔
createVisualWords(d, f);
iw.updateDocument(new Term(DocumentBuilder.FIELD_NAME_IDENTIFIER, d.getValues(DocumentBuilder.FIELD_NAME_IDENTIFIER)[0]), d);
if (pm != null) {
double len = (double) (end - start);
double percent = (double) (i - start) / len * 45d + 50;
pm.setProgress((int) percent);
pm.setNote("Creating visual words, ~" + (int) percent + "% finished");
}
// }
} catch (IOException e) {
e.printStackTrace();
}
}
}
}
private void createVisualWords(Document d, LireFeature f)
{
double[] tmpHist = new double[numClusters];
Arrays.fill(tmpHist, 0d);
IndexableField[] fields = d.getFields(localFeatureFieldName);
// remove the fields if they are already there ...
// 從索引中移除以下兩個字段以防已經存在
d.removeField(visualWordsFieldName);
d.removeField(localFeatureHistFieldName);
// find the appropriate cluster for each feature:
for (int j = 0; j < fields.length; j++) { // 獲取該描述符
f.setByteArrayRepresentation(fields[j].binaryValue().bytes, fields[j].binaryValue().offset, fields[j].binaryValue().length);
tmpHist[clusterForFeature((Histogram) f)]++; // 得到每一個特徵點所對應的最近聚類中心就+1
}
//quantize(tmpHist); // tmpHist就是最終的結果
d.add(new TextField(visualWordsFieldName, arrayToVisualWordString(tmpHist), Field.Store.YES)); // 以字符串的形式進行存儲，沒什麼用
d.add(new StoredField(localFeatureHistFieldName, SerializationUtils.toByteArray(tmpHist))); // 轉換成字節類型進行存儲
// remove local features to save some space if requested:
if (DELETE_LOCAL_FEATURES) {
d.removeFields(localFeatureFieldName); // 移除原有的field
}
// for debugging ..
// System.out.println(d.getValues(DocumentBuilder.FIELD_NAME_IDENTIFIER)[0] + " " + Arrays.toString(tmpHist));
}
private String getDuration(double time) {
double min = (System.currentTimeMillis() - time) / (1000 * 60);
double sec = (min - Math.floor(min)) * 60;
return String.format("%02d:%02d", (int) min, (int) sec);
}
public void setProgressMonitor(ProgressMonitor pm) {
this.pm = pm;
}
}KMeans.java(包含註釋)
package lmc.imageretrieval.imageanalysis.bovw;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.List;
import java.util.Set;
import lmc.imageretrieval.imageanalysis.Histogram;
import lmc.imageretrieval.utils.StatsUtils;
public class KMeans {
protected List<Image> images = new LinkedList<Image>();
protected int countAllFeatures = 0, numClusters = 256;
protected ArrayList<double[]> features = null;
protected Cluster[] clusters = null;
protected HashMap<double[], Integer> featureIndex = null;
public KMeans() {
}
public KMeans(int numClusters) {
this.numClusters = numClusters;
}
public void addImage(String identifier, List<double[]> features) { // 加入image
images.add(new Image(identifier, features));
countAllFeatures += features.size();
}
public int getFeatureCount() {
return countAllFeatures;
}
public void init() { // 聚類中心初始化
// create a set of all features:
features = new ArrayList<double[]>(countAllFeatures);
for (Image image : images) {
if (image.features.size() > 0) // 將所有的descriptor放入features中
for (double[] histogram : image.features) {
if (!hasNaNs(histogram)) features.add(histogram);
}
else {
System.err.println("Image with no features: " + image.identifier);
}
}
// --- check if there are (i) enough images and (ii) enough features
if (images.size() < 500) { // 圖片數量小於500 錯誤
System.err.println("WARNING: Please note that this approach has been implemented for big data and *a lot of images*. " +
"You might not get appropriate results with a small number of images employed for constructing the visual vocabulary.");
}
if (features.size() < numClusters*2) { // 特徵點個數不能小於聚類中心的兩倍
System.err.println("WARNING: Please note that the number of local features, in this case " + features.size() + ", is" +
"smaller than the recommended minimum number, which is two times the number of visual words, in your case 2*" + numClusters +
". Please adapt your data and either use images with more local features or more images for creating the visual vocabulary.");
}
if (features.size() < numClusters + 1) { //特徵點個數不能小於聚類中心+1
System.err.println("CRITICAL: The number of features is smaller than the number of clusters. This cannot work as there has to be at least one " +
"feature per cluster. Aborting process now.");
System.out.println("images: " + images.size());
System.out.println("features: " + features.size());
System.out.println("clusters: " + numClusters);
System.exit(1);
}
// find first clusters:
clusters = new Cluster[numClusters]; // 初始的聚類中心
Set<Integer> medians = selectInitialMedians(numClusters);
assert(medians.size() == numClusters); // this has to be the same ...
Iterator<Integer> mediansIterator = medians.iterator();
for (int i = 0; i < clusters.length; i++) {
double[] descriptor = features.get(mediansIterator.next());
clusters[i] = new Cluster(new double[descriptor.length]); // implicitly setting the length of the mean array.
System.arraycopy(descriptor, 0, clusters[i].mean, 0, descriptor.length);
}
}
protected Set<Integer> selectInitialMedians(int numClusters) {
return StatsUtils.drawSample(numClusters, features.size());
}
/**
* Do one step and return the overall stress (squared error). You should do this until
* the error is below a threshold or doesn't change a lot in between two subsequent steps.
*
* @return
*/
public double clusteringStep() { // 聚類迭代
for (int i = 0; i < clusters.length; i++) {
clusters[i].members.clear(); // 清空該聚類中心所有的成員
}
reOrganizeFeatures(); // 重新計算每個樣本點到聚類中心的距離，重新分配
recomputeMeans(); // 重新計算聚類中心的大小
return overallStress(); // 返回sum of squared 迭代結束指標
}
protected boolean hasNaNs(double[] histogram) { // 判斷是否有not a number
boolean hasNaNs = false;
for (int i = 0; i < histogram.length; i++) {
if (Double.isNaN(histogram[i])) {
hasNaNs = true;
break;
}
}
if (hasNaNs) {
System.err.println("Found a NaN in init");
// System.out.println("image.identifier = " + image.identifier);
for (int j = 0; j < histogram.length; j++) {
double v = histogram[j];
System.out.print(v + ", ");
}
System.out.println("");
}
return hasNaNs;
}
/**
* Re-shuffle all features.
*/
protected void reOrganizeFeatures() { // 重新計算每個點到聚類中心的距離，該點歸屬於哪一個聚類中心
for (int k = 0; k < features.size(); k++) { // 看k屬於哪個聚類中心最近
double[] f = features.get(k);
Cluster best = clusters[0];
double minDistance = clusters[0].getDistance(f);
for (int i = 1; i < clusters.length; i++) {
double v = clusters[i].getDistance(f); // 採用的是歐式距離
if (minDistance > v) {
best = clusters[i];
minDistance = v;
}
}
best.members.add(k);
}
}
/**
* Computes the mean per cluster (averaged vector)
*/
protected void recomputeMeans() { // 重新計算聚類中心
int length = features.get(0).length;
for (int i = 0; i < clusters.length; i++) {
Cluster cluster = clusters[i];
double[] mean = cluster.mean;
for (int j = 0; j < length; j++) {
mean[j] = 0;
for (Integer member : cluster.members) {
mean[j] += features.get(member)[j];
}
if (cluster.members.size() > 1)
mean[j] = mean[j] / (double) cluster.members.size();
}
if (cluster.members.size() == 1) { // 該聚類中心只含有一個點
System.err.println("** There is just one member in cluster " + i);
} else if (cluster.members.size() < 1) { // 該聚類中心沒有點
System.err.println("** There is NO member in cluster " + i);
// fill it with a random member?!?
int index = (int) Math.floor(Math.random()*features.size()); // 重新隨機選擇一個點作爲該聚類中心
System.arraycopy(features.get(index), 0, clusters[i].mean, 0, clusters[i].mean.length);
}
}
}
/**
* Squared error in classification.
*
* @return
*/
protected double overallStress() { // 計算聚類中的sum of squared
double v = 0;
int length = features.get(0).length;
for (int i = 0; i < clusters.length; i++) {
for (Integer member : clusters[i].members) {
float tmpStress = 0;
for (int j = 0; j < length; j++) {
// if (Float.isNaN(features.get(member).descriptor[j])) System.err.println("Error: there is a NaN in cluster " + i + " at member " + member);
tmpStress += Math.abs(clusters[i].mean[j] - features.get(member)[j]);
}
v += tmpStress;
}
}
return v;
}
public Cluster[] getClusters() {
return clusters;
}
public List<Image> getImages() {
return images;
}
/**
* Set the number of desired clusters.
*
* @return
*/
public int getNumClusters() {
return numClusters;
}
public void setNumClusters(int numClusters) {
this.numClusters = numClusters;
}
private HashMap<double[], Integer> createIndex() {
featureIndex = new HashMap<double[], Integer>(features.size());
for (int i = 0; i < clusters.length; i++) {
Cluster cluster = clusters[i];
for (Iterator<Integer> fidit = cluster.members.iterator(); fidit.hasNext(); ) {
int fid = fidit.next();
featureIndex.put(features.get(fid), i);
}
}
return featureIndex;
}
/**
* Used to find the cluster of a feature actually used in the clustering process (so
* it is known by the k-means class).
*
* @param f the feature to search for
* @return the index of the Cluster
*/
public int getClusterOfFeature(Histogram f) {
if (featureIndex == null) createIndex();
return featureIndex.get(f);
}
}
class Image {
public List<double[]> features;
public String identifier;
public float[] localFeatureHistogram = null;
private final int QUANT_MAX_HISTOGRAM = 256;
Image(String identifier, List<double[]> features) {
this.features = new LinkedList<double[]>();
this.features.addAll(features);
this.identifier = identifier;
}
public float[] getLocalFeatureHistogram() {
return localFeatureHistogram;
}
public void setLocalFeatureHistogram(float[] localFeatureHistogram) {
this.localFeatureHistogram = localFeatureHistogram;
}
public void initHistogram(int bins) {
localFeatureHistogram = new float[bins];
for (int i = 0; i < localFeatureHistogram.length; i++) {
localFeatureHistogram[i] = 0;
}
}
public void normalizeFeatureHistogram() { // 對聚類中心進行歸一化
float max = 0;
for (int i = 0; i < localFeatureHistogram.length; i++) {
max = Math.max(localFeatureHistogram[i], max);
}
for (int i = 0; i < localFeatureHistogram.length; i++) {
localFeatureHistogram[i] = (localFeatureHistogram[i] * QUANT_MAX_HISTOGRAM) / max;
}
}
public void printHistogram() {
for (int i = 0; i < localFeatureHistogram.length; i++) {
System.out.print(localFeatureHistogram[i] + " ");
}
System.out.println("");
}
}Cluster.java(包含註釋)
package lmc.imageretrieval.imageanalysis.bovw;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.util.Arrays;
import java.util.HashSet;
import lmc.imageretrieval.imageanalysis.Histogram;
import lmc.imageretrieval.utils.MetricsUtils;
import lmc.imageretrieval.utils.SerializationUtils;
public class Cluster implements Comparable<Object> {
double[] mean;
HashSet<Integer> members = new HashSet<Integer>();
private double stress = 0;
public Cluster() {
this.mean = new double[4 * 4 * 8];
Arrays.fill(mean, 0f);
}
public Cluster(double[] mean) {
this.mean = mean;
}
public String toString() {
StringBuilder sb = new StringBuilder(512);
for (Integer integer : members) {
sb.append(integer);
sb.append(", ");
}
for (int i = 0; i < mean.length; i++) {
sb.append(mean[i]);
sb.append(';');
}
return sb.toString();
}
public int compareTo(Object o) {
return ((Cluster) o).members.size() - members.size();
}
public double getDistance(Histogram f) {
return getDistance(f.getDoubleHistogram());
}
public double getDistance(double[] f) {
// L1
// return MetricsUtils.distL1(mean, f);
// L2
return MetricsUtils.distL2(mean, f);
}
/**
* Creates a byte array representation from the clusters mean.
*
* @return the clusters mean as byte array.
*/
public byte[] getByteRepresentation() {
return SerializationUtils.toByteArray(mean);
}
public void setByteRepresentation(byte[] data) {
mean = SerializationUtils.toDoubleArray(data);
}
public static void writeClusters(Cluster[] clusters, String file) throws IOException { // 將聚類中心寫入磁盤上
FileOutputStream fout = new FileOutputStream(file);
fout.write(SerializationUtils.toBytes(clusters.length)); // 聚類中心個數
fout.write(SerializationUtils.toBytes((clusters[0].getMean()).length)); // 聚類中心點的長度
for (int i = 0; i < clusters.length; i++) {
fout.write(clusters[i].getByteRepresentation()); // 寫入每個聚類中心
}
fout.close();
}
// TODO: re-visit here to make the length variable (depending on the actual feature size).
public static Cluster[] readClusters(String file) throws IOException { // 從磁盤上讀取聚類中心
FileInputStream fin = new FileInputStream(file);
byte[] tmp = new byte[4];
fin.read(tmp, 0, 4);
Cluster[] result = new Cluster[SerializationUtils.toInt(tmp)];
fin.read(tmp, 0, 4);
int size = SerializationUtils.toInt(tmp);
tmp = new byte[size * 8];
for (int i = 0; i < result.length; i++) {
int bytesRead = fin.read(tmp, 0, size * 8);
if (bytesRead != size * 8) System.err.println("Didn't read enough bytes ...");
result[i] = new Cluster();
result[i].setByteRepresentation(tmp);
}
fin.close();
return result;
}
public double getStress() {
return stress;
}
public void setStress(double stress) {
this.stress = stress;
}
public HashSet<Integer> getMembers() {
return members;
}
public void setMembers(HashSet<Integer> members) {
this.members = members;
}
/**
* Returns the cluster mean
*
* @return the cluster mean vector
*/
public double[] getMean() {
return mean;
}
}