mfeat-factors

Author: Source: Unknown - Please cite: The multi-feature digit dataset ------------------------------- Oowned and donated by: ---------------------- Robert P.W. Duin Department of Applied Physics Delft University of Technology P.O. Box 5046, 2600 GA Delft The Netherlands email: duin@ph.tn.tudelft.nl http : //www.ph.tn.tudelft.nl/~duin tel +31 15 2786143 Usage ----- A slightly different version of the database is used in M. van Breukelen, R.P.W. Duin, D.M.J. Tax, and J.E. den Hartog, Handwritten digit recognition by combined classifiers, Kybernetika, vol. 34, no. 4, 1998, 381-386. M. van Breukelen and R.P.W. Duin, Neural Network Initialization by Combined Classifiers, in: A.K. Jain, S. Venkatesh, B.C. Lovell (eds.), ICPR'98, Proc. 14th Int. Conference on Pattern Recognition (Brisbane, Aug. 16-20), The database as it is is used in: A.K. Jain, R.P.W. Duin, J. Mao, Statisitcal Pattern Recognition: A Review, in preparation Description ----------- This dataset consists of features of handwritten numerals (`0'--`9') extracted from a collection of Dutch utility maps. 200 patterns per class (for a total of 2,000 patterns) have been digitized in binary images. These digits are represented in terms of the following six feature sets (files): 1. mfeat-fou: 76 Fourier coefficients of the character shapes; 2. mfeat-fac: 216 profile correlations; 3. mfeat-kar: 64 Karhunen-Love coefficients; 4. mfeat-pix: 240 pixel averages in 2 x 3 windows; 5. mfeat-zer: 47 Zernike moments; 6. mfeat-mor: 6 morphological features. In each file the 2000 patterns are stored in ASCI on 2000 lines. The first 200 patterns are of class `0', followed by sets of 200 patterns for each of the classes `1' - `9'. Corresponding patterns in different feature sets (files) correspond to the same original character. The source image dataset is lost. Using the pixel-dataset (mfeat-pix) sampled versions of the original images may be obtained (15 x 16 pixels). Total number of instances: -------------------------- 2000 (200 instances per class) Total number of attributes: --------------------------- 649 (distributed over 6 datasets,see above) no missing attributes Total number of classes: ------------------------ 10 Format: ------ 6 files, see above. Each file contains 2000 lines, one for each instance. Attributes are SPACE separated and can be loaded by Matlab as > load filename No missing attributes. Some are integer, others are real. Information about the dataset CLASSTYPE: nominal CLASSINDEX: last