LIRE源代码分析 2：建立索引 提取特征向量 检索 [以颜色布局为例]

上一篇文章我们介绍了LIRE的基本接口，这篇我们来看一看它建立索引，提取特征向量和检索的过程。

一、建立索引(DocumentBuilder)

不同的特征向量提取方法的建立索引的类各不相同，它们都位于“net.semanticmetadata.lire.impl”中，如下图所示：

由图可见，每一种方法对应一个DocumentBuilder和一个ImageSearcher，类的数量非常的多，无法一一分析。在这里仅分析一个比较有代表性的：颜色布局。
颜色布局建立索引的类的名称是ColorLayoutDocumentBuilder，该类继承了AbstractDocumentBuilder，它的源代码如下所示：

package net.semanticmetadata.lire.impl;

import net.semanticmetadata.lire.AbstractDocumentBuilder;
import net.semanticmetadata.lire.DocumentBuilder;
import net.semanticmetadata.lire.imageanalysis.ColorLayout;
import net.semanticmetadata.lire.utils.ImageUtils;
import org.apache.lucene.document.Document;
import org.apache.lucene.document.Field;

import java.awt.image.BufferedImage;
import java.util.logging.Logger;

/**
 * Provides a faster way of searching based on byte arrays instead of Strings. The method
 * {@link net.semanticmetadata.lire.imageanalysis.ColorLayout#getByteArrayRepresentation()} is used
 * to generate the signature of the descriptor much faster.
 * User: Mathias Lux, [email protected]
 * Date: 30.06.2011
 */
public class ColorLayoutDocumentBuilder extends AbstractDocumentBuilder {
    private Logger logger = Logger.getLogger(getClass().getName());
    public static final int MAX_IMAGE_DIMENSION = 1024;

    public Document createDocument(BufferedImage image, String identifier) {
        assert (image != null);
        BufferedImage bimg = image;
        // Scaling image is especially with the correlogram features very important!
        // All images are scaled to guarantee a certain upper limit for indexing.
        if (Math.max(image.getHeight(), image.getWidth()) > MAX_IMAGE_DIMENSION) {
            bimg = ImageUtils.scaleImage(image, MAX_IMAGE_DIMENSION);
        }
        Document doc = null;
        logger.finer("Starting extraction from image [ColorLayout - fast].");
        ColorLayout vd = new ColorLayout();
        vd.extract(bimg);
        logger.fine("Extraction finished [ColorLayout - fast].");

        doc = new Document();
        doc.add(new Field(DocumentBuilder.FIELD_NAME_COLORLAYOUT_FAST, vd.getByteArrayRepresentation()));
        if (identifier != null)
            doc.add(new Field(DocumentBuilder.FIELD_NAME_IDENTIFIER, identifier, Field.Store.YES, Field.Index.NOT_ANALYZED));

        return doc;
    }
}

从源代码来看，其实主要就一个函数：createDocument(BufferedImage image, String identifier)，该函数的流程如下所示：

1.如果输入的图像分辨率过大（在这里是大于1024），则将图像缩小。

2.新建一个LireFeature类型的对象vd。

3.调用vd.extract()提取特征向量。

4.调用vd.getByteArrayRepresentation()获得特征向量。

5.将获得的特征向量加入Document，返回Document。

二、提取特征向量

在ColorLayoutDocumentBuilder中，使用了一个类型为ColorLayout的对象vd，并且调用了vd的extract()方法：

 ColorLayout vd = new ColorLayout();  
 vd.extract(bimg);  

此外调用了vd的getByteArrayRepresentation()方法：

new Field(DocumentBuilder.FIELD_NAME_COLORLAYOUT_FAST, vd.getByteArrayRepresentation())  

在这里我们看一看ColorLayout是个什么类。ColorLayout位于“net.semanticmetadata.lire.imageanalysis”包中，如下图所示：

由图可见，这个包中有很多的类。这些类都是以检索方法的名字命名的。我们要找的ColorLayout类也在其中。看看它的代码吧：

package net.semanticmetadata.lire.imageanalysis;

import net.semanticmetadata.lire.imageanalysis.mpeg7.ColorLayoutImpl;
import net.semanticmetadata.lire.utils.SerializationUtils;

/**
 * Just a wrapper for the use of LireFeature.
 * Date: 27.08.2008
 * Time: 12:07:38
 *
 * @author Mathias Lux, [email protected]
 */
public class ColorLayout extends ColorLayoutImpl implements LireFeature {

    /*
        public String getStringRepresentation() {
        StringBuilder sb = new StringBuilder(256);
        StringBuilder sbtmp = new StringBuilder(256);
        for (int i = 0; i < numYCoeff; i++) {
            sb.append(YCoeff[i]);
            if (i + 1 < numYCoeff) sb.append(' ');
        }
        sb.append("z");
        for (int i = 0; i < numCCoeff; i++) {
            sb.append(CbCoeff[i]);
            if (i + 1 < numCCoeff) sb.append(' ');
            sbtmp.append(CrCoeff[i]);
            if (i + 1 < numCCoeff) sbtmp.append(' ');
        }
        sb.append("z");
        sb.append(sbtmp);
        return sb.toString();
    }

    public void setStringRepresentation(String descriptor) {
        String[] coeffs = descriptor.split("z");
        String[] y = coeffs[0].split(" ");
        String[] cb = coeffs[1].split(" ");
        String[] cr = coeffs[2].split(" ");

        numYCoeff = y.length;
        numCCoeff = Math.min(cb.length, cr.length);

        YCoeff = new int[numYCoeff];
        CbCoeff = new int[numCCoeff];
        CrCoeff = new int[numCCoeff];

        for (int i = 0; i < numYCoeff; i++) {
            YCoeff[i] = Integer.parseInt(y[i]);
        }
        for (int i = 0; i < numCCoeff; i++) {
            CbCoeff[i] = Integer.parseInt(cb[i]);
            CrCoeff[i] = Integer.parseInt(cr[i]);

        }
    }
     */

    /**
     * Provides a much faster way of serialization.
     *
     * @return a byte array that can be read with the corresponding method.
     * @see net.semanticmetadata.lire.imageanalysis.CEDD#setByteArrayRepresentation(byte[])
     */
    public byte[] getByteArrayRepresentation() {
        byte[] result = new byte[2 * 4 + numYCoeff * 4 + 2 * numCCoeff * 4];
        System.arraycopy(SerializationUtils.toBytes(numYCoeff), 0, result, 0, 4);
        System.arraycopy(SerializationUtils.toBytes(numCCoeff), 0, result, 4, 4);
        System.arraycopy(SerializationUtils.toByteArray(YCoeff), 0, result, 8, numYCoeff * 4);
        System.arraycopy(SerializationUtils.toByteArray(CbCoeff), 0, result, numYCoeff * 4 + 8, numCCoeff * 4);
        System.arraycopy(SerializationUtils.toByteArray(CrCoeff), 0, result, numYCoeff * 4 + numCCoeff * 4 + 8, numCCoeff * 4);
        return result;
    }

    /**
     * Reads descriptor from a byte array. Much faster than the String based method.
     *
     * @param in byte array from corresponding method
     * @see net.semanticmetadata.lire.imageanalysis.CEDD#getByteArrayRepresentation
     */
    public void setByteArrayRepresentation(byte[] in) {
        int[] data = SerializationUtils.toIntArray(in);
        numYCoeff = data[0];
        numCCoeff = data[1];
        YCoeff = new int[numYCoeff];
        CbCoeff = new int[numCCoeff];
        CrCoeff = new int[numCCoeff];
        System.arraycopy(data, 2, YCoeff, 0, numYCoeff);
        System.arraycopy(data, 2 + numYCoeff, CbCoeff, 0, numCCoeff);
        System.arraycopy(data, 2 + numYCoeff + numCCoeff, CrCoeff, 0, numCCoeff);
    }

    public double[] getDoubleHistogram() {
        double[] result = new double[numYCoeff + numCCoeff * 2];
        for (int i = 0; i < numYCoeff; i++) {
            result[i] = YCoeff[i];
        }
        for (int i = 0; i < numCCoeff; i++) {
            result[i + numYCoeff] = CbCoeff[i];
            result[i + numCCoeff + numYCoeff] = CrCoeff[i];
        }
        return result;
    }

    /**
     * Compares one descriptor to another.
     *
     * @param descriptor
     * @return the distance from [0,infinite) or -1 if descriptor type does not match
     */

    public float getDistance(LireFeature descriptor) {
        if (!(descriptor instanceof ColorLayoutImpl)) return -1f;
        ColorLayoutImpl cl = (ColorLayoutImpl) descriptor;
        return (float) ColorLayoutImpl.getSimilarity(YCoeff, CbCoeff, CrCoeff, cl.YCoeff, cl.CbCoeff, cl.CrCoeff);
    }
}

ColorLayout类继承了ColorLayoutImpl类，同时实现了LireFeature接口。其中的方法大部分都是实现了LireFeature接口的方法。先来看看LireFeature接口是什么样子的：

/**
 * This is the basic interface for all content based features. It is needed for GenericDocumentBuilder etc.
 * Date: 28.05.2008
 * Time: 14:44:16
 *
 * @author Mathias Lux, [email protected]
 */
public interface LireFeature {
    public void extract(BufferedImage bimg);

    public byte[] getByteArrayRepresentation();

    public void setByteArrayRepresentation(byte[] in);

    public double[] getDoubleHistogram();

    float getDistance(LireFeature feature);

    java.lang.String getStringRepresentation();

    void setStringRepresentation(java.lang.String s);
}

注：这里没有注释了，仅能靠自己的理解了。

我简要概括一下自己对这些接口函数的理解：

1.extract(BufferedImage bimg)：提取特征向量

2.getByteArrayRepresentation()：获取特征向量（返回byte[]类型）

3.setByteArrayRepresentation(byte[] in)：设置特征向量（byte[]类型）

4.getDoubleHistogram()：

5.getDistance(LireFeature feature)：

6.getStringRepresentation()：获取特征向量（返回String类型）

7.setStringRepresentation(Java.lang.String s)：设置特征向量（String类型）

其中红色的是建立索引的过程中会用到的。

仔细看代码可以发现，所有的算法都实现了LireFeature接口，如下图所示：

三、检索(ImageSearcher)

我们继续看一下检索部分（ImageSearcher）。不同的方法的检索功能的类各不相同，它们都位于“net.semanticmetadata.lire.impl”中，如下图所示：

在这里仅分析一个比较有代表性的：颜色布局。前文已经分析过ColorLayoutDocumentBuilder，在这里我们分析一下ColorLayoutImageSearcher。源代码如下：

package net.semanticmetadata.lire.impl;

import net.semanticmetadata.lire.DocumentBuilder;
import net.semanticmetadata.lire.ImageDuplicates;
import net.semanticmetadata.lire.ImageSearchHits;
import net.semanticmetadata.lire.imageanalysis.ColorLayout;
import net.semanticmetadata.lire.imageanalysis.LireFeature;
import org.apache.lucene.document.Document;
import org.apache.lucene.index.IndexReader;

import java.io.FileNotFoundException;
import java.io.IOException;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.List;
import java.util.logging.Level;

/**
 * Provides a faster way of searching based on byte arrays instead of Strings. The method
 * {@link net.semanticmetadata.lire.imageanalysis.ColorLayout#getByteArrayRepresentation()} is used
 * to generate the signature of the descriptor much faster. First tests have shown that this
 * implementation is up to 4 times faster than the implementation based on strings
 * (for 120,000 images)
 * 

 * User: Mathias Lux, [email protected]
 * Date: 30.06 2011
 */
public class ColorLayoutImageSearcher extends GenericImageSearcher {
    public ColorLayoutImageSearcher(int maxHits) {
        super(maxHits, ColorLayout.class, DocumentBuilder.FIELD_NAME_COLORLAYOUT_FAST);
    }

    protected float getDistance(Document d, LireFeature lireFeature) {
        float distance = 0f;
        ColorLayout lf;
        try {
            lf = (ColorLayout) descriptorClass.newInstance();
            byte[] cls = d.getBinaryValue(fieldName);
            if (cls != null && cls.length > 0) {
                lf.setByteArrayRepresentation(cls);
                distance = lireFeature.getDistance(lf);
            } else {
                logger.warning("No feature stored in this document ...");
            }
        } catch (InstantiationException e) {
            logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
        } catch (IllegalAccessException e) {
            logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
        }

        return distance;
    }

    public ImageSearchHits search(Document doc, IndexReader reader) throws IOException {
        SimpleImageSearchHits searchHits = null;
        try {
            ColorLayout lireFeature = (ColorLayout) descriptorClass.newInstance();

            byte[] cls = doc.getBinaryValue(fieldName);
            if (cls != null && cls.length > 0)
                lireFeature.setByteArrayRepresentation(cls);
            float maxDistance = findSimilar(reader, lireFeature);

            searchHits = new SimpleImageSearchHits(this.docs, maxDistance);
        } catch (InstantiationException e) {
            logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
        } catch (IllegalAccessException e) {
            logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
        }
        return searchHits;
    }

    public ImageDuplicates findDuplicates(IndexReader reader) throws IOException {
        // get the first document:
        SimpleImageDuplicates simpleImageDuplicates = null;
        try {
            if (!IndexReader.indexExists(reader.directory()))
                throw new FileNotFoundException("No index found at this specific location.");
            Document doc = reader.document(0);

            ColorLayout lireFeature = (ColorLayout) descriptorClass.newInstance();
            byte[] cls = doc.getBinaryValue(fieldName);
            if (cls != null && cls.length > 0)
                lireFeature.setByteArrayRepresentation(cls);

            HashMap> duplicates = new HashMap>();

            // find duplicates ...
            boolean hasDeletions = reader.hasDeletions();

            int docs = reader.numDocs();
            int numDuplicates = 0;
            for (int i = 0; i < docs; i++) {
                if (hasDeletions && reader.isDeleted(i)) {
                    continue;
                }
                Document d = reader.document(i);
                float distance = getDistance(d, lireFeature);

                if (!duplicates.containsKey(distance)) {
                    duplicates.put(distance, new LinkedList());
                } else {
                    numDuplicates++;
                }
                duplicates.get(distance).add(d.getFieldable(DocumentBuilder.FIELD_NAME_IDENTIFIER).stringValue());
            }

            if (numDuplicates == 0) return null;

            LinkedList> results = new LinkedList>();
            for (float f : duplicates.keySet()) {
                if (duplicates.get(f).size() > 1) {
                    results.add(duplicates.get(f));
                }
            }
            simpleImageDuplicates = new SimpleImageDuplicates(results);
        } catch (InstantiationException e) {
            logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
        } catch (IllegalAccessException e) {
            logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
        }
        return simpleImageDuplicates;

    }
}

源代码里面重要的函数有3个：

float getDistance(Document d, LireFeature lireFeature)：

ImageSearchHits search(Document doc, IndexReader reader)：检索。最核心函数。

ImageDuplicates findDuplicates(IndexReader reader)：目前还没研究。

ColorLayoutImageSearcher继承了一个类——GenericImageSearcher，看一下GenericImageSearcher的源代码：

package net.semanticmetadata.lire.impl;

import net.semanticmetadata.lire.AbstractImageSearcher;
import net.semanticmetadata.lire.DocumentBuilder;
import net.semanticmetadata.lire.ImageDuplicates;
import net.semanticmetadata.lire.ImageSearchHits;
import net.semanticmetadata.lire.imageanalysis.LireFeature;
import net.semanticmetadata.lire.utils.ImageUtils;
import org.apache.lucene.document.Document;
import org.apache.lucene.index.IndexReader;

import java.awt.image.BufferedImage;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.List;
import java.util.TreeSet;
import java.util.logging.Level;
import java.util.logging.Logger;

/**
 * This file is part of the Caliph and Emir project: http://www.SemanticMetadata.net
 * 
Date: 01.02.2006
 * 
Time: 00:17:02
 *
 * @author Mathias Lux, [email protected]
 */
public class GenericImageSearcher extends AbstractImageSearcher {
    protected Logger logger = Logger.getLogger(getClass().getName());
    Class descriptorClass;
    String fieldName;

    private int maxHits = 10;
    protected TreeSet docs;

    public GenericImageSearcher(int maxHits, Class descriptorClass, String fieldName) {
        this.maxHits = maxHits;
        docs = new TreeSet();
        this.descriptorClass = descriptorClass;
        this.fieldName = fieldName;
    }

    public ImageSearchHits search(BufferedImage image, IndexReader reader) throws IOException {
        logger.finer("Starting extraction.");
        LireFeature lireFeature = null;
        SimpleImageSearchHits searchHits = null;
        try {
            lireFeature = (LireFeature) descriptorClass.newInstance();
            // Scaling image is especially with the correlogram features very important!
            BufferedImage bimg = image;
            if (Math.max(image.getHeight(), image.getWidth()) > GenericDocumentBuilder.MAX_IMAGE_DIMENSION) {
                bimg = ImageUtils.scaleImage(image, GenericDocumentBuilder.MAX_IMAGE_DIMENSION);
            }
            lireFeature.extract(bimg);
            logger.fine("Extraction from image finished");

            float maxDistance = findSimilar(reader, lireFeature);
            searchHits = new SimpleImageSearchHits(this.docs, maxDistance);
        } catch (InstantiationException e) {
            logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
        } catch (IllegalAccessException e) {
            logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
        }
        return searchHits;
    }

    /**
     * @param reader
     * @param lireFeature
     * @return the maximum distance found for normalizing.
     * @throws java.io.IOException
     */
    protected float findSimilar(IndexReader reader, LireFeature lireFeature) throws IOException {
        float maxDistance = -1f, overallMaxDistance = -1f;
        boolean hasDeletions = reader.hasDeletions();

        // clear result set ...
        docs.clear();

        int docs = reader.numDocs();
        for (int i = 0; i < docs; i++) {
            // bugfix by Roman Kern
            if (hasDeletions && reader.isDeleted(i)) {
                continue;
            }

            Document d = reader.document(i);
            float distance = getDistance(d, lireFeature);
            assert (distance >= 0);
            // calculate the overall max distance to normalize score afterwards
            if (overallMaxDistance < distance) {
                overallMaxDistance = distance;
            }
            // if it is the first document:
            if (maxDistance < 0) {
                maxDistance = distance;
            }
            // if the array is not full yet:
            if (this.docs.size() < maxHits) {
                this.docs.add(new SimpleResult(distance, d));
                if (distance > maxDistance) maxDistance = distance;
            } else if (distance < maxDistance) {
                // if it is nearer to the sample than at least on of the current set:
                // remove the last one ...
                this.docs.remove(this.docs.last());
                // add the new one ...
                this.docs.add(new SimpleResult(distance, d));
                // and set our new distance border ...
                maxDistance = this.docs.last().getDistance();
            }
        }
        return maxDistance;
    }

    protected float getDistance(Document d, LireFeature lireFeature) {
        float distance = 0f;
        LireFeature lf;
        try {
            lf = (LireFeature) descriptorClass.newInstance();
            String[] cls = d.getValues(fieldName);
            if (cls != null && cls.length > 0) {
                lf.setStringRepresentation(cls[0]);
                distance = lireFeature.getDistance(lf);
            } else {
                logger.warning("No feature stored in this document!");
            }
        } catch (InstantiationException e) {
            logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
        } catch (IllegalAccessException e) {
            logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
        }

        return distance;
    }

    public ImageSearchHits search(Document doc, IndexReader reader) throws IOException {
        SimpleImageSearchHits searchHits = null;
        try {
            LireFeature lireFeature = (LireFeature) descriptorClass.newInstance();

            String[] cls = doc.getValues(fieldName);
            if (cls != null && cls.length > 0)
                lireFeature.setStringRepresentation(cls[0]);
            float maxDistance = findSimilar(reader, lireFeature);

            searchHits = new SimpleImageSearchHits(this.docs, maxDistance);
        } catch (InstantiationException e) {
            logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
        } catch (IllegalAccessException e) {
            logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
        }
        return searchHits;
    }

    public ImageDuplicates findDuplicates(IndexReader reader) throws IOException {
        // get the first document:
        SimpleImageDuplicates simpleImageDuplicates = null;
        try {
            if (!IndexReader.indexExists(reader.directory()))
                throw new FileNotFoundException("No index found at this specific location.");
            Document doc = reader.document(0);

            LireFeature lireFeature = (LireFeature) descriptorClass.newInstance();
            String[] cls = doc.getValues(fieldName);
            if (cls != null && cls.length > 0)
                lireFeature.setStringRepresentation(cls[0]);

            HashMap> duplicates = new HashMap>();

            // find duplicates ...
            boolean hasDeletions = reader.hasDeletions();

            int docs = reader.numDocs();
            int numDuplicates = 0;
            for (int i = 0; i < docs; i++) {
                if (hasDeletions && reader.isDeleted(i)) {
                    continue;
                }
                Document d = reader.document(i);
                float distance = getDistance(d, lireFeature);

                if (!duplicates.containsKey(distance)) {
                    duplicates.put(distance, new LinkedList());
                } else {
                    numDuplicates++;
                }
                duplicates.get(distance).add(d.getFieldable(DocumentBuilder.FIELD_NAME_IDENTIFIER).stringValue());
            }

            if (numDuplicates == 0) return null;

            LinkedList> results = new LinkedList>();
            for (float f : duplicates.keySet()) {
                if (duplicates.get(f).size() > 1) {
                    results.add(duplicates.get(f));
                }
            }
            simpleImageDuplicates = new SimpleImageDuplicates(results);
        } catch (InstantiationException e) {
            logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
        } catch (IllegalAccessException e) {
            logger.log(Level.SEVERE, "Error instantiating class for generic image searcher: " + e.getMessage());
        }
        return simpleImageDuplicates;

    }

    public String toString() {
        return "GenericSearcher using " + descriptorClass.getName();
    }

}

下面来看看GenericImageSearcher中的search(BufferedImage image, IndexReader reader)函数的步骤（注：这个函数应该是用的最多的，输入一张图片，返回相似图片的结果集）：

1.输入图片如果尺寸过大（大于1024），则调整尺寸。

2.使用extract()提取输入图片的特征值。

3.根据提取的特征值，使用findSimilar()查找相似的图片。

4.新建一个ImageSearchHits用于存储查找的结果。

5.返回ImageSearchHits

在这里要注意一点：

GenericImageSearcher中创建特定方法的类的时候，使用了如下形式：

LireFeature lireFeature = (LireFeature) descriptorClass.newInstance();  

即接口的方式，而不是直接新建一个对象的方式，形如：

AutoColorCorrelogram acc = new AutoColorCorrelogram(CorrelogramDocumentBuilder.MAXIMUM_DISTANCE)  

相比而言，更具有通用型。

在search()函数中，调用了一个函数findSimilar()。这个函数的作用是查找相似图片的，分析了一下它的步骤：

1.使用IndexReader获取所有的记录

2.遍历所有的记录，和当前输入的图片进行比较，使用getDistance()函数

3.获取maxDistance并返回

在findSimilar()中，又调用了一个getDistance()，该函数调用了具体检索方法的getDistance()函数。

下面我们来看一下ColorLayout类中的getDistance()函数：

    public float getDistance(LireFeature descriptor) {  
            if (!(descriptor instanceof ColorLayoutImpl)) return -1f;  
            ColorLayoutImpl cl = (ColorLayoutImpl) descriptor;  
            return (float) ColorLayoutImpl.getSimilarity(YCoeff, CbCoeff, CrCoeff, cl.YCoeff, cl.CbCoeff, cl.CrCoeff);  
        }  

发现其调用了ColorLayoutImpl类中的getSimilarity()函数：

    public static double getSimilarity(int[] YCoeff1, int[] CbCoeff1, int[] CrCoeff1, int[] YCoeff2, int[] CbCoeff2, int[] CrCoeff2) {  
            int numYCoeff1, numYCoeff2, CCoeff1, CCoeff2, YCoeff, CCoeff;  
      
            //Numbers of the Coefficients of two descriptor values.  
            numYCoeff1 = YCoeff1.length;  
            numYCoeff2 = YCoeff2.length;  
            CCoeff1 = CbCoeff1.length;  
            CCoeff2 = CbCoeff2.length;  
      
            //take the minimal Coeff-number  
            YCoeff = Math.min(numYCoeff1, numYCoeff2);  
            CCoeff = Math.min(CCoeff1, CCoeff2);  
      
            setWeightingValues();  
      
            int j;  
            int[] sum = new int[3];  
            int diff;  
            sum[0] = 0;  
      
            for (j = 0; j < YCoeff; j++) {  
                diff = (YCoeff1[j] - YCoeff2[j]);  
                sum[0] += (weightMatrix[0][j] * diff * diff);  
            }  
      
            sum[1] = 0;  
            for (j = 0; j < CCoeff; j++) {  
                diff = (CbCoeff1[j] - CbCoeff2[j]);  
                sum[1] += (weightMatrix[1][j] * diff * diff);  
            }  
      
            sum[2] = 0;  
            for (j = 0; j < CCoeff; j++) {  
                diff = (CrCoeff1[j] - CrCoeff2[j]);  
                sum[2] += (weightMatrix[2][j] * diff * diff);  
            }  
      
            //returns the distance between the two desciptor values  
      
            return Math.sqrt(sum[0] * 1.0) + Math.sqrt(sum[1] * 1.0) + Math.sqrt(sum[2] * 1.0);  
        }  

由代码可见，getSimilarity()通过具体的算法，计算两张图片特征向量之间的相似度。