文件列表:

data\1.tiff (132412, 1999-01-11)
data\10.tiff (125176, 1999-01-11)
data\11.tiff (114122, 1999-01-11)
data\12.tiff (132442, 1999-01-11)
data\13.tiff (130596, 1999-01-11)
data\2.tiff (131714, 1999-01-11)
data\3.tiff (131774, 1999-01-11)
data\4.tiff (132262, 1999-01-11)
data\5.tiff (131830, 1999-01-11)
data\6.tiff (124028, 1999-01-11)
data\7.tiff (132386, 1999-01-11)
data\8.tiff (132236, 1999-01-11)
data\9.tiff (132594, 1999-01-11)
data\Thumbs.db (41984, 2007-07-02)
code\data.m (2570, 1999-09-06)
code\demo.m (3830, 1999-09-16)
code\estimate.m (9216, 1999-09-16)
code\visual.asv (924, 2007-07-15)
code\visual.m (923, 2007-07-15)
data (0, 2007-09-28)
results (0, 2007-09-28)
code (0, 2007-09-28)

------------------------------------------------------------------------- Topographic ICA & Independent Subspace Analysis, applied on image patches as explained in: Hyvrinen, A. and Hoyer, P. "Emergence of Phase and Shift Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces". Neural Computation. To appear. Hyvrinen, A. and Hoyer, P. "Topographic Independent Component Analysis". Submitted. Package by Patrik Hoyer (September 1999). ------------------------------------------------------------------------- -------- CONTENTS -------- 1. WHY DID WE WRITE THIS PACKAGE? 2. HOW TO USE IT 3. HARDWARE REQUIREMENTS 4. ANY QUESTIONS? --------------------------------- 1. WHY DID WE WRITE THIS PACKAGE? --------------------------------- The purpose of this package is to provide data and code for estimating the proposed models from image data. This is to allow fellow researchers to duplicate our results and to ease the application of this method to other datasets. ---------------- 2. HOW TO USE IT ---------------- First and foremost, read this whole file first. Especially the section on hardware requirements. Below is the directory structure of the package: code/ data.m - code for gathering the patches from the images estimate.m - the algorithms for estimating the basis visual.m - displays the found bases demo.m - usage examples data/ The original gray-scale images in TIFF format results/ all results are stored here in MATLAB "*.mat" data files Estimating bases for image windows is easy. To launch the demo examples, move to the 'code' directory, and use the MATLAB commands >> demo(1); >> demo(2); >> demo(3); to estimate the ICA/ISA/TICA models (respectively). Each estimation will continue indefinitely, writing the current state to disk every five iterations. At any time you can use >> demo(4); to display the results so far. Note that the ICA estimation converges much faster than the other models due to the nice properties of the FastICA algorithm used. Also, the ISA estimation is faster than TICA due to lower computational costs. ------------------------ 3. COMPUTER REQUIREMENTS ------------------------ The estimations with large windows (example code #1-4 in 'demo.m') require on the order of 600 megabytes of memory. This large number is due partly to MATLAB's way of handling memory, partly to the fact that the code was written with clarity, not memory-efficiency, as a main objective. However, the estimation with small windows (#5-8) use much less memory and may be a good place to start. Even with the memory, the large window runs take long to converge to nice-looking filters. The ICA estimation requires something like a hundred iterations, the ISA needs around 200 iterations, and TICA on the order of 300. Again, if you do not have the computational power for that (or you would like results quickly), you can run the example codes #5-8 which are not as computationally demanding (although the results are not as nice due to small window size). This package was written for MATLAB version 5.3, although it will probably work well both with earlier and later versions. ----------------- 4. ANY QUESTIONS? ----------------- Please send any questions about the models or the code to aapo.hyvarinen@hut.fi or patrik.hoyer@hut.fi Patrik Hoyer Sept. 15th, 1999.

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