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

# halcon

halcon 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."

## Installation

To install halcon run

pip3 install --user halcon


## 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 and feature selection is beyond the scope of this package. There are many feature calculation/machine learning packages out there that you might find useful, like

• 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 1) cityblock (Manhattan distance) and 2) 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

$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 FALCON 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


## Bugs and Questions

To submit bugs about the source code visit

To submit bugs about the documentation visit

For any other inquiries visit those links as well.

## Project details

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