CBCL FACE DATABASE #1（CBCL人脸数据库＃1）
英文关键词：
faces and non-faces,the Center for Biological and Computational Learni,MIT,Recognition,
中文关键词：
脸和非脸部中心生物和计算Learni，麻省理工学院，识别，
数据格式：
IMAGE
数据介绍：
This is a database of faces and non-faces, that has been used extensively at the Center for Biological and Computational Learning at MIT. It is freely available for research use. If you use this database in published work, you must reference:
CBCL Face Database #1
MIT Center For Biological and Computation Learning
https://www.doczj.com/doc/2810599091.html,/projects/cbcl
CONTENTS:
Training set: 2,429 faces, 4,548 non-faces
Test set: 472 faces, 23,573 non-faces
The training set face were generated for [3]. The training set non-faces were generated for [2]. The test set is a subset of the CMU Test Set 1 [4], information about how the subset was chosen can be found in [2].
The tarballs train.tar.gz and test.tar.gz contain subdirectories with the images stored in individual pgm files. These pgm images can be processed using the functions in the c-code directory. Note that the c-code directory is not a complete program, merely a collection of functions that are useful for reading in the pgm files. These functions have successfully been used numerous times to read in and convert this data; we are unlikely to have time to respond to requests regarding help with these functions, or offer other programming help.
For those who don't want to work with the image files directly, we provide the files svm.train.normgrey and svm.test.normgrey. These files are in the proper format to be read by the SvmFu SVM solver
(https://www.doczj.com/doc/2810599091.html,/projects/cbcl/), but should be easily convertible
for use by other algorithms. In each file, the first line contains the number of examples (6,977 training, 24,045 test), the second line contains the number of dimensions (19x19 = 361). Each additional line consists of a single image, histogram equalized and normalized so that all pixel values are between 0 and 1, followed by a 1 for a face or a -1 for a non-face. This is the data that was used in [1].
A slightly modified version of the dataset was used in [2] --- the histogram normalization was identical, but additionally, the corners of the image were masked out.
[以下内容来由机器自动翻译]
这是已被广泛用于中心生物及计算学习在麻省理工学院的脸和非脸部的数据库。它是自由供研究使用。如果您使用此数据库中发表的作品，你必须引用：CBCL 脸数据库# 1麻省理工学院生物研究中心和计算学习https://www.doczj.com/doc/2810599091.html,/projects/cbcl内容：训练集：2,429 脸、4,548 非脸测试集：472 脸、23,573 非脸[3] 生成的训练集的脸。训练设置非-面临的产生[2]。测试集是一个子集的cmu 中央结算系统测试设置1 [4]、[2] 中可以找到有关如何选择子集的信息。压缩档train.tar.gz 和只有个别pgm 文件中存储的图像中包含子目录。可以使用的c 代码目录中的功能来处理这些pgm 图像。请注
意c 代码目录不是一个完整的程序，只是对于阅读pgm 文件中非常有用的功能的集合。这些函数成功曾经无数次在读取并转换此数据；我们不可能有时间去响应请求关于帮助这些函数，或提供其他编程帮助。对于那些不想直接使用的图像文件，我们提供的文件svm.train.normgrey 和svm.test.normgrey。这些文件被读取
（https://www.doczj.com/doc/2810599091.html,/projects/cbcl/），SvmFu SVM 规划求解以正确的方式是，但应该轻松自由兑换其他算法使用。在每个文件中，第一行包含示例24,045 测试6,977 培训），第二行包含的维度（19 x 19 = 361) 数的数。其他每行包含单个图像，直方图均衡和归一化，使所有像素值都为0 和1 之间的后面的一张脸1 或非工作面-1。这是[1] 中使用的数据。[2] 中使用数据集的一个稍加修改的版本---直方图正常化是相同的但此外，图像的边角被遮盖掉。
[自动翻译由Microsoft Translator完成]
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