Python implementation of FALCON: Feedback Adaptive Loop for Content-Based Retrieval

## halcon

halcon (falcon in Spanish) is a python implementation of the Feedback Adaptive Loop for Content-Based Retrieval (FALCON) algorithm as described in

• Leejay Wu, Christos Faloutsos, Katia P. Sycara, and Terry R. Payne. 2000. FALCON: Feedback Adaptive Loop for Content-Based Retrieval. In Proceedings of the 26th International Conference on Very Large Data Bases (VLDB ‘00), Amr El Abbadi, Michael L. Brodie, Sharma Chakravarthy, Umeshwar Dayal, Nabil Kamel, Gunter Schlageter, and Kyu-Young Whang (Eds.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 297-306.

FALCON is, as described in the article abstract, “a novel method that is designed to handle disjunctive queries within metric spaces. The user provides weights for positive examples; our system ‘learns’ the implied concept and returns similar objects.”

## Pre-Requisites

• numpy

• scipy

To install the prerequisites in Ubuntu 12.04

sudo apt-get install update
sudo apt-get install python-numpy python-scipy

## Installation

There are several ways to install halcon. The most common way is to download the source code, unzip/untar the source code package and run the command

sudo python setup.py install

I have plans of submitting this package to the Python Package Index. If I do so, then should be able to install it by running the command

sudo pip install halcon

COMMENT: halcon depends on numpy and scipy. Installing these packages in Windows and MacOSX is not a trivial task. For more information refer to the documentation.

If you wish to install halcon in a virtual enviroment, then you can do

virtualenv halcon
cd halcon
source ./bin/activate
pip install numpy
pip install scipy
mkdir src
cd src
git clone git@github.com:icaoberg/halcon.git
cd halcon
python setup.py install
cd ../../
deactivate

COMMENT: The previous snippet assumes that you have virtualenv installed in your working system.

## Usage

There is only one method that you need to know about

halcon.search.query(good_set, candidates, alpha=-5,
metric='euclidean', normalization='zscore', debug=False)

Here is a brief description of each of the input arguments

• good_set and candidates are two lists of lists where each member of both lists has the same shape.

record = [<identifier>, <initial_score>, <feature_vector>]

For example in wine.py, I download a CSV file where the first feature_vector looks like this

[1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065]

and then I modify it like this

good_set = []
identifier = 'wine00'
initial_score = 1
feature_vector = [1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065]
good_set.append([identifier, initial_score, feature_vector])

For more information about the definition of the initial score, please refer to the article. In all my examples I use a initial score of 1, that is, all images have the same weight. The identifier should be unique (though not enforced), so you can tell images apart. This package assumes every object is represented by a feature vector. Feature calculation goes beyond the scope of this package. There are many feature calculation/machine learning packages out there that you might find useful, like OpenCV, mahotas and SLIC.

• alpha. For more information about alpha, please refer to the article. The recommended value by the paper is -5, which is the default value used in this package.

• metric. In the research article, a measure of distance d is used to calculate the distance between two feature vectors. The default value is euclidean (Euclidean distance) and other supported metrics are cityblock (Manhattan distance) and hamming (Hamming distance).

• normalization. Feature normalization option. Default is zscore. Alternative option is standard.

• debug. If debug flag is on, then it should print more information about the calculation as they happen.

## Examples

For convenience and testing I included some examples. These examples download some datasets from the web and use them to trigger a query. The only exception is the random feature vectors example. For example, to run the iris example simply run in terminal

python examples/iris.py

The examples have a dependency that the package does not, since I use tabulate to pretty print the results from the examples.

In my humble opinion, the best way to run the examples is using virtualenv -which is what I do for travis-. The next commands assume you have virtualenv available.

virtualenv halcon --system-site-packages
. ./halcon/bin/activate
cd halcon
mkdir src
cd src
pip install numpy
pip install scipy
pip install tabulate
git clone https://github.com/icaoberg/halcon.git
cd halcon
python setup.py install
cd ..
python examples/iris.py

$python examples/iris.py This example uses the iris dataset from Machine Learning Repository Center for Machine Learning and Intelligent Systems http://archive.ics.uci.edu/ml/datasets/Iris I will use the first feature vector as my query image [[0, 1, array([ 5.1, 3.5, 1.4, 0.2, 1. ])]] And I will use the rest of the feature vectors to find the most similar images Now notice that feature vector with iid1 has the same values iid0 [1, 1, array([ 5.1, 3.5, 1.4, 0.2, 1. ])] So I expect that if halcon is working correctly, then iid1 should be the top hit! Elapsed time: 0.0221660137177 seconds Ranking Identifier Class Score --------- ------------ --------------- ----------- 0 1 Iris-setosa 0 1 28 Iris-setosa 1.27788e-43 2 5 Iris-setosa 2.40121e-40 3 29 Iris-setosa 2.40121e-40 4 40 Iris-setosa 5.83391e-40 5 8 Iris-setosa 7.04398e-39 6 18 Iris-setosa 1.1259e-35 7 41 Iris-setosa 1.51906e-34 8 50 Iris-versicolor 6.99696e-34 9 37 Iris-setosa 1.09221e-32 10 12 Iris-setosa 1.22203e-32 11 49 Iris-setosa 2.05046e-32 12 11 Iris-setosa 4.25801e-31 13 21 Iris-setosa 6.55842e-31 14 47 Iris-setosa 5.54098e-29 15 36 Iris-setosa 7.93943e-29 16 7 Iris-setosa 2.16985e-28 17 20 Iris-setosa 4.23544e-28 18 25 Iris-setosa 1.67453e-27 19 3 Iris-setosa 2.40919e-27 Do the top results in the list above belong to the same class as the query image? If so, then SCORE! It seems to work. ### wine.py $ python examples/wine.py
This example uses the wine dataset from
Machine Learning Repository
Center for Machine Learning and Intelligent Systems
http://archive.ics.uci.edu/ml/datasets/Wine
I will use the first three feature vectors as my query wine set
And I will use the rest of the feature vectors to find the most similar images
Elapsed time: 0.0280928611755 seconds

Ranking  Identifier          Score
---------  ------------  -----------
0  wine1         0
1  wine2         0
2  wine3         0
3  wine21        2.77663e-05
4  wine30        0.000629879
5  wine23        0.00252617
6  wine49        0.00318536
7  wine57        0.00456123
8  wine36        0.0152067
9  wine39        0.0197516
10  wine58        0.0243848
11  wine9         0.024467
12  wine55        0.045762
13  wine24        0.046893
14  wine7         0.113906
15  wine45        0.188355
16  wine27        0.201802
17  wine41        0.206469
18  wine31        0.288536
19  wine56        0.291853

$python examples/metrics.py This example uses the wine dataset from Machine Learning Repository Center for Machine Learning and Intelligent Systems http://archive.ics.uci.edu/ml/datasets/Wine This example uses this dataset to compare the different metrics available in halcon Ranking Euclidean City Block Hamming --------- ----------- ------------ --------- 0 wine1 wine1 wine1 1 wine21 wine21 wine5 2 wine57 wine57 wine47 3 wine41 wine23 wine3 4 wine23 wine30 wine9 5 wine30 wine41 wine17 6 wine45 wine49 wine25 7 wine10 wine55 wine30 8 wine48 wine9 wine36 9 wine7 wine7 wine39 10 wine36 wine36 wine41 11 wine55 wine10 wine45 12 wine56 wine45 wine52 13 wine52 wine56 wine2 14 wine3 wine48 wine4 15 wine43 wine47 wine6 16 wine9 wine52 wine7 17 wine49 wine3 wine8 18 wine29 wine17 wine10 19 wine8 wine8 wine11 COMMENT: Hamming distance is meant for comparing strings so this example does not make a lot of sense since these features do not represent characters. These are not meant to be conclusions, rather, these are observations. ### random_feature_vectors.py $ python examples/random_feature_vectors.py
Generating random query image
Query image name: img
Elapsed time: 7.39097595215e-05 seconds
Generating random dataset
Elapsed time: 0.00141191482544 seconds
Querying with one image
Elapsed time: 0.0233750343323 seconds
Top Ten Results!
Ranking  Identifier          Score
---------  ------------  -----------
0  img           0
1  8             1.30582e+14
2  85            2.70987e+14
3  25            3.68567e+14
4  97            6.19091e+14
5  11            6.54178e+14
6  70            6.55048e+14
7  91            6.89901e+14
8  79            7.17429e+14

$python examples/number_of_feature_vectors_performance-euclidean_distance.py Generating and querying on synthetic datasets, please wait... These are the results from this test Number of Feature Vectors Time (in seconds) --------------------------- ------------------- 100 0.0247221 200 0.0378191 300 0.0665781 400 0.0999439 500 0.123964 600 0.120883 700 0.138576 800 0.176096 900 0.180116 There is a clear trend that is dependent on the number of feature vectors. You know what? Why don't we try making a pretty plot as well COMMENT: the examples are not seeded so you might get different results. These are not meant to be conclusions, rather, these are observations. ### number_of_features_performance-euclidean_distance.py $ python examples/number_of_features_performance-euclidean_distance.py
Generating and querying on synthetic datasets, please wait...                   ]

These are the results from this test

Number of Features    Time (in seconds)
--------------------  -------------------
50            0.0666399
100            0.0619891
150            0.0683651
200            0.0779331
250            0.077204
300            0.0829229
350            0.087312
400            0.092144
450            0.09745
500            0.111081
550            0.112051
600            0.11652
650            0.119202
700            0.123624
750            0.127126
800            0.134157
850            0.138586
900            0.149411
950            0.14823

There seems to be trend that is dependent on the number of feature vectors.
You know what? Why don't we try making a pretty plot as well

COMMENT: the examples are not seeded so you might get different results. These are not meant to be conclusions, rather, these are observations.

## Documentation

Documentation was written using Sphinx. To generate documentation use the following commands.

To generate html

cd docs
make html

To generate PDF document

cd docs
make latexpdf

To generate epub document

cd docs
make epub

## Bugs and Questions

To submit bugs about the source code visit

https://github.com/icaoberg/halcon

To submit bugs about the documentation visit

https://github.com/icaoberg/halcon-docs

For any other inquiries visit those links as well.

## Project details

Uploaded source