LIRe 源代码分析 7:算法类[以颜色布局为例]
=====================================================
LIRe源代码分析系列文章列表:
LIRe 源代码分析 2:基本接口(DocumentBuilder)
LIRe 源代码分析 3:基本接口(ImageSearcher)
LIRe 源代码分析 4:建立索引(DocumentBuilder)[以颜色布局为例]
LIRe 源代码分析 6:检索(ImageSearcher)[以颜色布局为例]
=====================================================
前面关于LIRe的文章,介绍的都是架构方面的东西,没有细研究具体算法。本文以颜色布局为例,介绍一下算法类的实现。
颜色布局描述符以一种非常紧密的形式有效地表示了图像的颜色空间分布信息。它以非常小的计算代价, 带来高的检索效率。因此, 颜色布局特征在视频镜头关键帧提取中有很重要的意义。颜色布局提取方法如下:
1 将图像从RGB 空间映射到YCbCr空间, 映射公式为
Y= 0.299* R + 0.587* G + 0.114* B
Cb= - 0.169* R – 0.331* G + 0.500* B
Cr = 0.500* R –0.419* G – 0.081* B
2 将整幅图像分成64块, 每块尺寸为(W /8) *(H/8), 其中W 为整幅图像的宽度, H 为整幅图像的高度, 计算每一块中所有像素的各个颜色分量( Y, Cb, Cr )的平均值, 并以此作为该块的代表颜色( Y, Cb, Cr );
3 对帧图像中各块的颜色分量平均值进行DCT 变换, 得到各分量的一系列DCT 系数;
4 对各分量的DCT 系数, 通过之字形扫描和量化, 取出各自DCT 变换的低频分量, 这三组低频分量共同构成该帧图像的颜色布局描述符。
颜色布局算法的实现位于ColorLayoutImpl类中,该类处于“net.semanticmetadata.lire.imageanalysis.mpeg7”包中,如图所示:
ColorLayoutImpl类的代码量很大,很多地方都还没有研究,在这里仅展示部分已经看过的代码:
/* 雷霄骅 * 中国传媒大学/数字电视技术 * leixiaohua1020@126.com * */ /** * Class for extrcating & comparing MPEG-7 based CBIR descriptor ColorLayout * * @author Mathias Lux, mathias@juggle.at */ public class ColorLayoutImpl { // static final boolean debug = true; protected int[][] shape; protected int imgYSize, imgXSize; protected BufferedImage img; protected static int[] availableCoeffNumbers = {1, 3, 6, 10, 15, 21, 28, 64}; //特征向量(Y,Cb,Cr) public int[] YCoeff; public int[] CbCoeff; public int[] CrCoeff; //特征向量的大小 protected int numCCoeff = 28, numYCoeff = 64; protected static int[] arrayZigZag = { 0, 1, 8, 16, 9, 2, 3, 10, 17, 24, 32, 25, 18, 11, 4, 5, 12, 19, 26, 33, 40, 48, 41, 34, 27, 20, 13, 6, 7, 14, 21, 28, 35, 42, 49, 56, 57, 50, 43, 36, 29, 22, 15, 23, 30, 37, 44, 51, 58, 59, 52, 45, 38, 31, 39, 46, 53, 60, 61, 54, 47, 55, 62, 63 }; protected static double[][] arrayCosin = { ... }; protected static int[][] weightMatrix = new int[3][64]; protected BufferedImage colorLayoutImage; /** * init used by all constructors */ private void init() { shape = new int[3][64]; YCoeff = new int[64]; CbCoeff = new int[64]; CrCoeff = new int[64]; colorLayoutImage = null; extract(); } public void extract(BufferedImage bimg) { this.img = bimg; imgYSize = img.getHeight(); imgXSize = img.getWidth(); init(); } private void createShape() { int y_axis, x_axis; int i, k, x, y, j; long[][] sum = new long[3][64]; int[] cnt = new int[64]; double yy = 0.0; int R, G, B; //init of the blocks for (i = 0; i < 64; i++) { cnt[i] = 0; sum[0][i] = 0; sum[1][i] = 0; sum[2][i] = 0; shape[0][i] = 0; shape[1][i] = 0; shape[2][i] = 0; } WritableRaster raster = img.getRaster(); int[] pixel = {0, 0, 0}; for (y = 0; y < imgYSize; y++) { for (x = 0; x < imgXSize; x++) { raster.getPixel(x, y, pixel); R = pixel[0]; G = pixel[1]; B = pixel[2]; y_axis = (int) (y / (imgYSize / 8.0)); x_axis = (int) (x / (imgXSize / 8.0)); k = (y_axis << 3) + x_axis; //RGB to YCbCr, partition and average-calculation yy = (0.299 * R + 0.587 * G + 0.114 * B) / 256.0; sum[0][k] += (int) (219.0 * yy + 16.5); // Y sum[1][k] += (int) (224.0 * 0.564 * (B / 256.0 * 1.0 - yy) + 128.5); // Cb sum[2][k] += (int) (224.0 * 0.713 * (R / 256.0 * 1.0 - yy) + 128.5); // Cr cnt[k]++; } } for (i = 0; i < 8; i++) { for (j = 0; j < 8; j++) { for (k = 0; k < 3; k++) { if (cnt[(i << 3) + j] != 0) shape[k][(i << 3) + j] = (int) (sum[k][(i << 3) + j] / cnt[(i << 3) + j]); else shape[k][(i << 3) + j] = 0; } } } } ......(其他代码都已经省略) private int extract() { createShape(); Fdct(shape[0]); Fdct(shape[1]); Fdct(shape[2]); YCoeff[0] = quant_ydc(shape[0][0] >> 3) >> 1; CbCoeff[0] = quant_cdc(shape[1][0] >> 3); CrCoeff[0] = quant_cdc(shape[2][0] >> 3); //quantization and zig-zagging for (int i = 1; i < 64; i++) { YCoeff[i] = quant_ac((shape[0][(arrayZigZag[i])]) >> 1) >> 3; CbCoeff[i] = quant_ac(shape[1][(arrayZigZag[i])]) >> 3; CrCoeff[i] = quant_ac(shape[2][(arrayZigZag[i])]) >> 3; } setYCoeff(YCoeff); setCbCoeff(CbCoeff); setCrCoeff(CrCoeff); return 0; } /** * Takes two ColorLayout Coeff sets and calculates similarity. * * @return -1.0 if data is not valid. */ 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); } public int getNumberOfCCoeff() { return numCCoeff; } public void setNumberOfCCoeff(int numberOfCCoeff) { this.numCCoeff = numberOfCCoeff; } public int getNumberOfYCoeff() { return numYCoeff; } public void setNumberOfYCoeff(int numberOfYCoeff) { this.numYCoeff = numberOfYCoeff; } 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]); } } public int[] getYCoeff() { return YCoeff; } public int[] getCbCoeff() { return CbCoeff; } public int[] getCrCoeff() { return CrCoeff; } }
下面介绍几个主要的函数:
提取:
1.extract(BufferedImage bimg):提取特征向量的函数,里面调用了init()。
2.init():初始化了 YCoeff,CbCoeff, CrCoeff。调用extract()(注意这个extract()是没有参数的)
3.extract():完成了提取特征向量的过程,其中调用了createShape()。
4.createShape():未研究。
获取/设置特征向量(注意:有参数为String和byte[]两种类型的特征向量,按照原代码里的说法,byte[]的效率要高一些):
1.getStringRepresentation():获取特征向量
2.setStringRepresentation():设置特征向量
计算相似度:
getSimilarity(int[] YCoeff1, int[] CbCoeff1, int[] CrCoeff1, int[] YCoeff2, int[] CbCoeff2, int[] CrCoeff2)
主要的变量:
3个存储特征向量(Y,Cb,Cr)的数组:
public int[] YCoeff; public int[] CbCoeff; public int[] CrCoeff;
特征向量的大小:
protected int numCCoeff = 28, numYCoeff = 64;
LIRe 源代码分析 7:算法类[以颜色布局为例]的更多相关文章
- LIRe 源代码分析 6:检索(ImageSearcher)[以颜色布局为例]
===================================================== LIRe源代码分析系列文章列表: LIRe 源代码分析 1:整体结构 LIRe 源代码分析 ...
- LIRe 源代码分析 5:提取特征向量[以颜色布局为例]
===================================================== LIRe源代码分析系列文章列表: LIRe 源代码分析 1:整体结构 LIRe 源代码分析 ...
- LIRe 源代码分析 4:建立索引(DocumentBuilder)[以颜色布局为例]
===================================================== LIRe源代码分析系列文章列表: LIRe 源代码分析 1:整体结构 LIRe 源代码分析 ...
- 转:LIRe 源代码分析
1:整体结构 LIRE(Lucene Image REtrieval)提供一种的简单方式来创建基于图像特性的Lucene索引.利用该索引就能够构建一个基于内容的图像检索(content- based ...
- LIRe 源代码分析 3:基本接口(ImageSearcher)
===================================================== LIRe源代码分析系列文章列表: LIRe 源代码分析 1:整体结构 LIRe 源代码分析 ...
- LIRe 源代码分析 2:基本接口(DocumentBuilder)
===================================================== LIRe源代码分析系列文章列表: LIRe 源代码分析 1:整体结构 LIRe 源代码分析 ...
- LIRe 源代码分析 1:整体结构
===================================================== LIRe源代码分析系列文章列表: LIRe 源代码分析 1:整体结构 LIRe 源代码分析 ...
- Hadoop源代码分析
http://wenku.baidu.com/link?url=R-QoZXhc918qoO0BX6eXI9_uPU75whF62vFFUBIR-7c5XAYUVxDRX5Rs6QZR9hrBnUdM ...
- Hadoop源代码分析(完整版)
Hadoop源代码分析(一) 关键字: 分布式云计算 Google的核心竞争技术是它的计算平台.Google的大牛们用了下面5篇文章,介绍了它们的计算设施. GoogleCluster:http:// ...
随机推荐
- list标准函数的模拟
;反序 ( ) -> ( ) (define (rvs x) (let recur ((x x)(res '())) (if (null? x) res (recur (cdr x) (cons ...
- javap反编译命令详解&Eclipse中配置javap命令
javap命令所有参数如下图所示: javap 命令用于解析类文件.其输出取决于所用的选项.若没有使用选项,javap 将输出传递给它的类的 public 域及方法.javap 将其输出到标准输出设备 ...
- [Matlab]技巧笔记
1.将字符串作为Matlab命令执行 md = 'dir'; eval(md); 2.将字符串作为系统命令执行 md = 'dir'; system(md); 3.使显示图像的坐标轴使用相同的度量单位 ...
- 01_数据库连接池,数据源,ResultSetMetaData,jdbc优化
一.数据库连接池 1. 什么是连接池 传统的开发模式下,Servlet处理用户的请求,找Dao查询数据,dao会创建与数据库之间的连接,完成数据查询后会关闭数据库的链接. 这样的方式会导致用户每 ...
- springMVC系列之(四) spring+springMVC+hibernate 三大框架整合
首先我们要知道Hibernate五大对象:,本实例通过深入的使用这五大对象和spring+springMVC相互结合,体会到框架的好处,提高我们的开发效率 Hibernate有五大核心接口,分别是:S ...
- Linux下which、whereis、locate、find 命令查找文件
转自:http://blog.csdn.net/gh320/article/details/17411743 我们经常在linux要查找某个文件,但不知道放在哪里了,可以使用下面的一些命令来搜索 ...
- 从一个简洁的进度刻度绘制中了解自定义View的思路流程
先看效果(原谅我的渣像素),进度的刻度.宽度.颜色可以随意设定: [项目github地址: https://github.com/zhangke3016/CircleLoading] 实现起来并不难, ...
- iOS中 Realm的学习与使用 韩俊强的博客
每日更新关注:http://weibo.com/hanjunqiang 新浪微博! 有问题或技术交流可以咨询!欢迎加入! 这篇直接搬了一份官方文档过来看的 由于之前没用markdown搞的乱七八糟的 ...
- Ext JS 6开发实例(四) :调整主视图
上文把主界面设置好,但是主视图因为界面的微调出现了显示问题,本文将把它调整好了. 打开app/view/main/Main.js,可以看到主视图是派生于标签面板(Ext.tab.Panel)的.在视图 ...
- 1082. Read Number in Chinese (25)
题目如下: Given an integer with no more than 9 digits, you are supposed to read it in the traditional Ch ...