==============================================================================
Copyright 2005
Center for Biological & Computational Learning at MIT and MIT
All rights reserved.
Permission to copy and modify this data, software, and its documentation only
for internal research use in your organization is hereby granted, provided
that this notice is retained thereon and on all copies. This data and software
should not be distributed to anyone outside of your organization without
explicit written authorization by the author(s) and MIT. It should not be
used for commercial purposes without specific permission from the authors
and MIT. MIT also requires written authorization by the author(s) to publish
results obtained with the data or software and possibly citation of relevant
CBCL reference papers.
We make no representation as to the suitability and operability of this data
or software for any purpose. It is provided "as is" without express or implied
warranty.
==============================================================================
This directory contains a new implementation of the methods described in:
T. Serre, L. Wolf and T. Poggio. Object Recognition with Features
Inspired by Visual Cortex. To Appear In: Proceedings of 2005 IEEE
Computer Society Conference on Computer Vision and Pattern
Recognition (CVPR 2005), IEEE Computer Society Press, San Diego,
June 2005.
A more complete AI Memo version is available at
(ftp://publications.ai.mit.edu/ai-publications/2004/AIM-2004-026.pdf).
This new implementation is faster and easier to use than the original
one, and was tested to be almost fully compatible with it. Lior Wolf,
Stan Bileschi and Thomas Serre contributed to this implementation
which is based on the C implementation written by Thomas. Parts of
that C implementation were based on an earlier C code by Max
Riesenhuber,
BEFORE YOU RUN
==============
1. Set the directories for the positive and negative images. There are
four directories: train_set.pos, train_set.net, test_set.pos and
test_set.net. These are set at lines 18-21 of demoRelease.m
2. The input images have to be grayscale. Although the algorithm has
some invariance to scale, it might be a good idea to scale all images
to the same height. We use a height of 140 pixels in many of our
experiments.
3. If you would like to use an SVM classifier, please install OSU SVM
(http://www.ece.osu.edu/~maj/osu_svm/). Add the path of OSU SVM to the
second line of demoRelease. If you'd prefer a NN classifier set useSVM
(line 5) to zero.
4. If you'd like the algorithm to learn its own object-specific features
set READPATCHESFROMFILE (line 9) to zero (should give somewhat better
results on many datasets). If not, you can use the universal features
that are stored in PatchesFromNaturalImages250per4sizes.mat. This is done
by setting READPATCHESFROMFILE to one. The stored prototypes are taken
from a set of "natural images" we collected.
Contents of the directory:
MAIN DEMO
=========
demoRelease.m - a demo showing a possible use of the code in this
directory. The demo reads a set of images, extracts the C2 features
for this set, and builds a classifier to learn the class of images.
MAIN STANDARD MODEL FUNCTIONS
=============================
C1.m - extracts s1 and c1 layers of the standard model representation
C2.m - extracts s2 and c2 layers of the standard model representation
CLASSIFICATION FUNCTIONS
========================
CLSnn.m - Nearest Neighbor classifier train
CLSnnC.m - Nearest Neighbor classifier test
CLSosusvm.m - SVM train (a wrapper function for osusvm)
CLSosusvmC.m - SVM test (a wrapper function for osusvm)
UTILITY FUNCTIONS
=================
extractC2forcell.m - extracts C2 for all images in a cell, using
all prototypes in another cell
extractRandC1Patches.m - extracts random C1 patches to serve as prototypes
init_gabor.m - creates Gabor filters
maxfilter.m - local maximum in an image
padimage.m - adds zeros around the boundary of the image
readAllImages.m - reads all images in training and testing
directories into one cell
sumfilter.m - local sums in an image
unpadimage.m - reverses the effect of padimage
WindowedPatchDistance.m - scans an image looking for the best match
for a prototype (image fragment)
PRECOMPUTED UNIVERSAL FEATURES
==============================
PatchesFromNaturalImages250per4sizes.mat
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
BUG FIXES
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Jul 01 2005 - changed bugs in extractC2forcell, extractRandC1Patches.m
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AdaBoost等MatLab代码(带测试数据).zip
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AdaBoost等MatLab代码(带测试数据).zip (250个子文件)
drawBoostSeriesFeaSelectResult.asv 6KB
testDSDetect.asv 4KB
ForwardSearchFeaSelect.asv 4KB
ConfuseDiseaseAreaRecognition.asv 3KB
blockColorDistribute.asv 3KB
windowRecognition.asv 3KB
AdaBoost.asv 3KB
TestWindowUnionEx.asv 2KB
GrayRunLength.asv 2KB
analysisHypothesisFeature.asv 2KB
FloatBoost.asv 2KB
limitedTrainAdaBoostLearner.asv 2KB
ImageBlockRecognizedByHMax.asv 2KB
testTrainSamples.asv 2KB
ImageBlockRecognizedByColorHMax.asv 2KB
avgValue.asv 1KB
TestBG.asv 1KB
bootstrapTest.asv 1KB
isSampleSimilarToCankerModel.asv 999B
fitness.asv 458B
testLimitedTrainAdaBoostLearner.asv 437B
CankerFeatureCluster.asv 191B
OrangeDiagnose.m 12KB
trainCascadeAdaBoost.m 10KB
trainSceBoostLearner.m 10KB
featureNameMap.m 9KB
GrayGradsComatrixProps.m 9KB
trainAsfCascadeAdaBoost.m 9KB
trainAdaBoostLearner.m 8KB
trainFloatBoostLearner.m 8KB
graycopropsex.m 8KB
extractColorFeature.m 8KB
identifySeriesFeatureByIndex.m 7KB
trainAsfCascadeAdaBoost2.m 7KB
identifyFeatureByIndex.m 7KB
DecisionTree.m 7KB
FloatBoost2.m 6KB
drawBoostSeriesFeaSelectResult.m 6KB
imageZoom.m 6KB
MultiBoost.m 5KB
PyramidAnalysis.m 5KB
GrayRunLengthProps.m 5KB
trainRealBoostLearner.m 5KB
L_FaceDetection.m 5KB
grayDifferStatProps.m 5KB
C1.m 5KB
testAdaBoostLearner.m 5KB
testBoostLearner.m 5KB
RTrainSamples.m 5KB
testSceBoostExample.m 4KB
GrayGradsComatrix.m 4KB
InvariantMomentProps.m 4KB
testDSDetect.m 4KB
TestWindowUnionEx.m 4KB
BoostSeriesFeaSelect.m 4KB
geneNegativeSamples.m 4KB
ConfuseDiseaseAreaRecognition.m 4KB
MoveWindowing.m 4KB
L_partfeature.m 4KB
ForwardSearchFeaSelect.m 4KB
extractGrayDifferStatFeature.m 4KB
extractGaborFeature.m 4KB
extractCoOccureTextFeature.m 4KB
testPyramidAnalysis.m 4KB
TestHMax.m 3KB
demoRelease.m 3KB
AdaBoostTrainResultShow.m 3KB
areaRecognitionExEX.m 3KB
calDetectRate.m 3KB
getAreaCankerSamples.m 3KB
searchBestWeakLearner.m 3KB
dispHMaxSegmentResult.m 3KB
testFeaSelectionForClassify.m 3KB
AdaBoostDecisionForImageBlock.m 3KB
AdaBoostClassfy.m 3KB
FeaSelectComparasion.m 3KB
windowRecognitionEx.m 3KB
FeaSubsetCascadeAdaBoost.m 3KB
testHarrLikeFea.m 3KB
extractFeature.m 3KB
AdaBoostBasedSingleFeaSelect.m 3KB
testAdaBoost.m 3KB
detectDuplicated.m 3KB
RealBoostClassfy.m 3KB
windowRecognition.m 3KB
GaborProps.m 3KB
ClassicalFeaSelect.m 3KB
testBoost.m 3KB
testColorHMaxPyramid.m 3KB
ImageBlockRecogByCascadeAdaBoost.m 3KB
testANN.m 3KB
blockColorDistribute.m 3KB
GATest.m 3KB
extractGRLMFeature.m 3KB
extractInvariantMomentFeature.m 3KB
extractDirsWinsowFeatures.m 3KB
findBestWeakLearner.m 3KB
testCascadeAdaBoost.m 3KB
areaRecognition.m 3KB
trainWindowSamples.m 3KB
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