Contents
- Introduction
- Hard Code Word
- Results
- Locality-constrained Linear
- Results
- Grid Search
- Sequential Hierarchy Classifier
- Manually assigned clusters
- Results
- Clusters from K-means
- Results
- Other Dataset Evaluation
- Birds
- Butterflies
- Other Experiments
- Results
- Scene Datasets
- Code
- Git Logs
- References
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Running the Code
The software implementation was done in Matlab.
The code package can be downloaded from here.
Standard Scene classification:
This functionality is implemented in the code folder.
Syntax:
MainSuper( mainDir , imgCount, testName, useLLC, useKer, delOld,
dictionarySize, pyramidLevls, numTextonImages, patchSize, gridSpacing, k );
where:
mainDir: Name of the directory containing the images which will be classified
imgCount: The number of images for each scene to be used in training
testName: Name of the output files
useLLC: 0 for non-LLC, 1 to use LLC
useKer: 0 without kernel, 1 is with kernel
delOld: 0 to keep the .mat files generated from BuildPyramid, 1 to delete those files
dictionarySize: The size of the dictionary used in BuildPyramid, standard is 1024
pyramidLevels: Number of levels for spatial pyramid
numTextonImages: Number of texton images used to create histograms
patchSize: the size of each patch for the sift descriptor
gridSpacing: the space between dense sift samples
k: The number of nearest neighbors to use for LLC
Output in the form of matrices (confusion matrix, accuracy, mean accuracy, etc.)
will be generated in a folder named vars one level up from mainDir. The start
of the names for these files will be given by the testName parameter entered:
Multi level classification:
This functionality is implemented in the code folder.
Syntax:
MainTree( mainDir , imgCount, testName, useLLC, useKer, delOld,
dictionarySize, pyramidLevls, numTextonImages, patchSize, gridSpacing, k );
where the parameters and output are the same as above.
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