媒体特征提取.docx

1、媒体特征提取媒体计算方法课程实验总结报告实验名称： 媒体特征提取 学 号： 姓 名： 日 期： 2014-12-23 一、实验目的1、实验目标以图像媒体为例，对指定的图像数据进行颜色特征、形状特征、局部不变等特征提取处理，体会不同的图像特征提取方法的特点和适用范围。2、实验涉及到的学习内容颜色特征提取、形状特征提取、局部不变特征提取（SIFT）。二、实验具体完成情况1、总体实验方案利用matlab编程实现对图像的颜色特征提取、形状特征提取和局部不变特征提取(SIFT)。2、具体技术途径matlab图像处理函数。3、实验结果与分析（1）颜色特征提取编写图像的灰度直方图特征提取算法，实现与imHi

2、st函数类似的效果。算法思想：利用for循环对原始图像进行遍历，统计每个颜色值在图像中的像素个数，根据统计结果绘制直方图。实验结果图1 本算法绘制的原始图像的直方图图2 imhist函数绘制的原始图像直方图（2）形状特征提取通过边缘检测等方法提取简单图像的边界特征，并用链码的方式保存图像的形状特征。算法思想：对原始图像进行边缘提取，在得到的边缘特征矩阵中寻找一个非零点，即边缘像素点，从该点开始，沿顺时针方向遍历其相邻8个像素点，记录第一个非零点的相对位置。再遍历这个边缘像素点周围的像素点，寻找下一个边缘像素点，直到重新遍历到初始点停止。实验结果图3 原始图像图4 提取的图像边缘特征算法只提取了

3、图像的边缘特征，并直接调用matlab的fchcode函数对图像边缘特征链码表示。（3）局部不变特征提取对于选定的某幅图像，提取SIFT局部不变特征，体会该特征的旋转、尺度、光照等不变性。算法思想：构建原始图像高斯金字塔；再同尺度相邻相差构建差分高斯金字塔；寻找尺度空间极值点，并删除错误点，其余为关键点；计算关键点坐标和所处的尺度；以关键点邻域梯度的主方向作为该点的方向特征。这里是使用别人写好的SIFT特征提取类函数，自己对其进行调用来实现特征提取以及匹配。实验结果图5 原始图像图6 提取的原始图像的SIFT特征信息图7 相似图像的SIFT特征匹配三、存在的主要问题和建议三个小的实验算法思想并

4、不难，但是在实现过程中发现自己的编程水平还有欠缺，理解了算法的思想，但是由于技术所限不能够按照算法思想自己编写一套程序。只能调用别人写好的函数实现自己预想的功能。另外此次实验都是在matlab中实现的，但在以后的工程实践中可能更多的是使用opencv，所以接下来要学习一下opencv的使用，现已在自己电脑的vs2010上配置好了opencv2.4.2，自己会在学习使用opencv的过程中重新实现以上实验。附：程序核心源代码（1）颜色特征提取close all%imhist直方图特征提取f1 = imread(Fig1202(c)(WashingtonDC_Band3_512).tif);sub

5、plot(1,2,1), imshow(f1), title(WashingtonDC_Band);subplot(1,2,2), imhist(f1), title(imhist); %自己编写imhist提取函数m,n=size(f1);a=zeros(1,256);for i=1:m for j=1:n a(f1(i,j)+1)=a(f1(i,j)+1)+1; endendfigure();subplot(1,2,1), imshow(f1), title(WashingtonDC_Band);subplot(1,2,2),bar(a,b)；axis(0 255 0 max(a)+100

6、0) ,title(myself-imhist);（2）形状特征提取f=imread( Fig1107(a)(mapleleaf).tif);g=edge(f,sobel);B=boundaries(g);b=B1,1;c=fchcode(b);（3）尺度不变特征提取(Demo见附件)%sift.m% image, descriptors, locs = sift(imageFile)% This function reads an image and returns its SIFT keypoints.% Input parameters:% imageFile: the file nam

7、e for the image.% Returned:% image: the image array in double format% descriptors: a K-by-128 matrix, where each row gives an invariant% descriptor for one of the K keypoints. The descriptor is a vector% of 128 values normalized to unit length.% locs: K-by-4 matrix, in which each row has the 4 value

8、s for a% keypoint location (row, column, scale, orientation). The % orientation is in the range -PI, PI radians.% Credits: Thanks for initial version of this program to D. Alvaro and % J.J. Guerrero, Universidad de Zaragoza (modified by D. Lowe) function image, descriptors, locs = sift(imageFile) %

9、Load imageimage = imread(imageFile); % If you have the Image Processing Toolbox, you can uncomment the following% lines to allow input of color images, which will be converted to grayscale.% if isrgb(image)% image = rgb2gray(image);% end rows, cols = size(image); % Convert into PGM imagefile, readab

10、le by keypoints executablef = fopen(tmp.pgm, w);if f = -1 error(Could not create file tmp.pgm.);endfprintf(f, P5n%dn%dn255n, cols, rows);fwrite(f, image, uint8);fclose(f); % Call keypoints executableif isunix command = !./sift ;else command = !siftWin32 ;endcommand = command tmp.key;eval(command); %

11、 Open tmp.key and check its headerg = fopen(tmp.key, r);if g = -1 error(Could not open file tmp.key.);endheader, count = fscanf(g, %d %d, 1 2);if count = 2 error(Invalid keypoint file beginning.);endnum = header(1);len = header(2);if len = 128 error(Keypoint descriptor length invalid (should be 128)

12、.);end % Creates the two output matrices (use known size for efficiency)locs = double(zeros(num, 4);descriptors = double(zeros(num, 128); % Parse tmp.keyfor i = 1:num vector, count = fscanf(g, %f %f %f %f, 1 4); %row col scale ori if count = 4 error(Invalid keypoint file format); end locs(i, :) = ve

13、ctor(1, :); descrip, count = fscanf(g, %d, 1 len); if (count = 128) error(Invalid keypoint file value.); end % Normalize each input vector to unit length descrip = descrip / sqrt(sum(descrip.2); descriptors(i, :) = descrip(1, :);endfclose(g); %showkeys.m% showkeys(image, locs)% This function display

14、s an image with SIFT keypoints overlayed.% Input parameters:% image: the file name for the image (grayscale)% locs: matrix in which each row gives a keypoint location (row,% column, scale, orientation) function showkeys(image, locs) disp(Drawing SIFT keypoints .); % Draw image with keypointsfigure(P

15、osition, 50 50 size(image,2) size(image,1);colormap(gray);imagesc(image);hold on;imsize = size(image);for i = 1: size(locs,1) % Draw an arrow, each line transformed according to keypoint parameters. TransformLine(imsize, locs(i,:), 0.0, 0.0, 1.0, 0.0); TransformLine(imsize, locs(i,:), 0.85, 0.1, 1.0

16、, 0.0); TransformLine(imsize, locs(i,:), 0.85, -0.1, 1.0, 0.0);endhold off; % - Subroutine: TransformLine -% Draw the given line in the image, but first translate, rotate, and% scale according to the keypoint parameters.% Parameters:% Arrays:% imsize = rows columns of image% keypoint = subpixel_row

17、subpixel_column scale orientation% Scalars:% x1, y1; begining of vector% x2, y2; ending of vectorfunction TransformLine(imsize, keypoint, x1, y1, x2, y2) % The scaling of the unit length arrow is set to approximately the radius% of the region used to compute the keypoint descriptor.len = 6 * keypoin

18、t(3); % Rotate the keypoints by ori = keypoint(4)s = sin(keypoint(4);c = cos(keypoint(4); % Apply transformr1 = keypoint(1) - len * (c * y1 + s * x1);c1 = keypoint(2) + len * (- s * y1 + c * x1);r2 = keypoint(1) - len * (c * y2 + s * x2);c2 = keypoint(2) + len * (- s * y2 + c * x2); line(c1 c2, r1 r

19、2, Color, c);%match.m% num = match(image1, image2)% This function reads two images, finds their SIFT features, and% displays lines connecting the matched keypoints. A match is accepted% only if its distance is less than distRatio times the distance to the% second closest match.% It returns the numbe

20、r of matches displayed.% Example: match(scene.pgm,book.pgm); function num = match(image1, image2) % Find SIFT keypoints for each imageim1, des1, loc1 = sift(image1);im2, des2, loc2 = sift(image2); % For efficiency in Matlab, it is cheaper to compute dot products between% unit vectors rather than Euc

21、lidean distances. Note that the ratio of % angles (acos of dot products of unit vectors) is a close approximation% to the ratio of Euclidean distances for small angles.% distRatio: Only keep matches in which the ratio of vector angles from the% nearest to second nearest neighbor is less than distRat

22、io.distRatio = 0.6; % For each descriptor in the first image, select its match to second image.des2t = des2; % Precompute matrix transposefor i = 1 : size(des1,1) dotprods = des1(i,:) * des2t; % Computes vector of dot products vals,indx = sort(acos(dotprods); % Take inverse cosine and sort results %

23、 Check if nearest neighbor has angle less than distRatio times 2nd. if (vals(1) 0) line(loc1(i,2) loc2(match(i),2)+cols1, . loc1(i,1) loc2(match(i),1), Color, c); endendhold off;num = sum(match 0);fprintf(Found %d matches.n, num);%appendimages.m% im = appendimages(image1, image2)% Return a new image

24、 that appends the two images side-by-side. function im = appendimages(image1, image2) % Select the image with the fewest rows and fill in enough empty rows% to make it the same height as the other image.rows1 = size(image1,1);rows2 = size(image2,1); if (rows1 rows2) image1(rows2,1) = 0;else image2(rows1,1) = 0;end % Now append both images side-by-side.im = image1 image2;