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LIBPMK: A Pyramid Match Toolkit

Author: John J. Lee
Project home page: http://people.csail.mit.edu/libpmk/jjl

This mega-package contains the core LIBPMK distribution as well as
several extension libraries. Each extension is in a separate directory
and comes with its own LICENSE file. For more information, please see
the project home page.

This file contains a brief description of the LIBPMK core and each
extension. It also gives instructions on how to build the library and
gives pointers to the included demo tools.

==================================================================
1. LIBPMK CORE (directory name: libpmk2)
==================================================================

This builds the LIBPMK core library, as well as the set of packaged
tools that lets you run a PMK.

1. cd libpmk2
2. make libpmk 
3. make libpmk_util
4. make tools  # OPTIONAL
5. make diag   # OPTIONAL
6. make tests  # OPTIONAL

Steps 2 and 3 build the core library.

Step 4 builds some pre-packaged tools that lets you run the PMK
pipeline without writing any code, as well as a lot of other useful
data manipulation tools (the comments at the top of each file describe
what each tool does). The resulting binaries are all present in the
"tools" subdirectory.

Step 5 builds a set of diagnostic tools that lets you inspect the
contents of a PointSetList file, a pyramid file, a kernel file, or the
output of a hierarchical cluster. It is great for debugging. The
resulting binaries all go to the "diagnostic" subdirectory.

Step 6 builds unit tests for various pieces of infrastructure. They
are provided in case you want to make modifications to the basic data
structures without breaking anything.


==================================================================
2. EXAMPLE USAGE OF FULL PMK PIPELINE (directory name: libpmk_eth)
==================================================================

This provides a demo example of an end-to-end experiment that uses the
interchangeable LIBPMK components in a single binary on the ETH-80
dataset. It performs hierarchical clustering to make a vocabulary,
creates pyramids, compares them and builds a kernel matrix, then
trains and tests an SVM.

0. Go to http://people.csail.mit.edu/jjl/libpmk/samples/eth.html and download
   the files listed under the "Data files" section.
1. cd libpmk_eth
2. make
3. ./eth-experiment.out /path/to/ETH80_GridSIFT.psl 3 11 /path/to/ETH80_labels.txt

Example output:
Reading /afs/csail.mit.edu/u/j/jjl/data/eth/ethGridPCASIFTptsets.bin
Running hierarchical clustering
Making pyramids
Computing kernel matrix
Average accuracy: 0.847500


==================================================================
3. EXTENSION: FEATURE EXTRACTION (directory name: libpmk_features)
==================================================================

This provides the feature extraction functionality of LIBPMK. It
requires ImageMagick to be installed on the system. In addition, the
binaries provided in this package are provided courtesy of other
researchers (please see the LICENSE and README files in the
libpmk_features directory for references and copyright rules) and run
on Linux only.

0. sudo apt-get install libmagick++-dev  (Ubuntu)
0. Make sure libpmk and libpmk_util from LIBPMK core are compiled (Section 1)
1. cd libpmk_features
2. make library
3. make tools (OPTIONAL)

Step 2 creates libpmk_features.o, which other programs can link to.

Step 3 builds a demo application that reads image filenames from a
command line and produces a PointSetList. Run it without any command
line arguments to see example usage.

==================================================================
4. EXTENSION: SPATIAL PMK (directory name: spatial_pmk)
==================================================================

This provides the Spatial PMK algorithm of Lazebnik et al.

0. Make sure libpmk and libpmk_util from LIBPMK core are compiled (Section 1)
1. cd spatial_pmk
2. make
3. make tools (OPTIONAL)
4. make tests (OPTIONAL)

Step 2 builds the object files for the spatial PMK extension. They are used
by the tools in step 3.

Step 3 builds binaries that use the object files from step 2. They comprise
a full run of the spatial PMK algorithm. Please see the comments at the top
of each file to see a description of what they do. You may also run any
of the binaries without commandline arguments to see example usage.

Step 4 builds a unit test (optional).

==================================================================
5. EXTENSION: ISM DEMO (directory name: libpmk_ism)
==================================================================

This provides the Implicit Shape Model implementation of Liebe et al.,
and also provides a demo GTK application that showcases it. This demo
sssssssssrequires the Feature Extraction extension (Section 2) to be built, and
also requires that libgtkmm is installed on the system (for the demo
UI).

0. Make sure libpmk and libpmk_util from LIBPMK core are compiled (Section 1)
0. Make sure the libpmk_features library is built (Section 3)
0. sudo apt-get install libgtkmm-2.4-dev   (Ubuntu)
1. cd libpmk_ism
2. make library
3. make ui (OPTIONAL)

Optionally, you may also build some example tools that do NOT use a
UI, but just some binaries that you can use to train and test a model
from the command line:
4. cd examples
5. make

As with everything above, the compiled binaries can be run without
commandline options to see example usage. Also, descriptions are
available at the top of each file.

To run the demo UI (step 3), run "demo.out" in the "ui"
subdirectory. This package contains a couple of example images you may
use. Load an image using the UI, then drag a box around a region, then
add it to the training set. Then, you may load another image and click
"Detect objects".

NOTE: in the UI demo, the paths to the extractor binares are hard
coded.  If you encounter problems while trying to compute features of
an image while running this application, you may have to edit frame.cc
and image-display.cc to manually set the paths to the "h_affine.ln"
and the "compute_descriptors.ln" binaries provided by the feature
extractor extension (Section 2).

==================================================================
6. EXTENSION: ADAPTIVE VOCABULARY FORESTS (directory name: avt)
==================================================================

Provides the Adaptive Vocabulary Forests of Yeh et al.

0. Make sure libpmk and libpmk_util from LIBPMK core are compiled (Section 1)
1. cd avt
2. make library
3. make tools (OPTIONAL)

As with everything else: the files in the "tools" subdirectory can be
run without arguments to see example usage, and their descriptions are
documented at the top of each file. The included tools allow you to
build vocabulary trees, then create pyramids from them so that they
can be matched and turned into a kernel via the LIBPMK core tools.