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Scalable Vocabulary Trees for Object Recognition
Group 13, CS676A, IITK
Assignment 3: REPORT
======================================================================================
We implemented Scalable Vocabulary Tree for Object Recognition as described in the 
paper. This implementation is in 'PYTHON 3' with 'OPENCV 3.1'. 
If your machine is not set up with PYTHON 3 OPENCV, please use the 'install' script 
to install the neccessary libraries/opencv etc., so that you can test our 
implementation.


IMPLEMENTATION DETAILS
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1. Extract SIFT keypoints and their feature descriptors for all images in the dataset 
   and dump them in a list.
2. Now, construct vocabulary tree using heirarchical KMeans clustering. (We have used
   MiniBatchKMeans, a faster implementation of the KMeans algorithm).
3. After we have constructed the vocabulary tree, we map images to the leaf nodes of 
   the vocabulary tree. We do this by looking up the SIFT descriptors of the image in 
   the tree by comparing the euclidean distances with cluster centers values at each 
   node of the tree.
4. Now, for a query image, we extract the SIFT descriptors and map it to different 
   leaf node of the vocabulary tree. After that we use the TFID scoring as described in 
   the paper to score all the images present in the test database. Then, we report the 
   best four matches for the query image.
5. Now, if all the four reported images come from the group same as that of the query 
   image, we say that the result is 100% accurate. Otherwise, it can be 75%, 50%, 25% 
   or 0%.
6. To infer the accuracy of the results at a given branch factor or depth, we take the 
   average of the accuracies observed for all the query images.


RESULTS
======================================================================================
We have tabulated the results from the experiment in the following format:
****************************************************
  B     mD    [ A1     A2     A3     A4 ]    ACC
****************************************************
B  : Branch Factor
mD : Maximum Depth
A1 : Accuracy of the best matches
A2 : Accuracy of the second best matches
A3 : Accuracy of the third best matches
A4 : Accuracy of the fourth best matches
ACC: Overall accuracy

Accuracy Measure
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We know that the object categories are grouped as sets of four images. So, for 
accuracy measure we say that the resultis 100% accurate if all of the four best 
matches come from the corresponding sets.


DATASET SIZE : 50 (We started with small set for preliminary analysis)
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Varying branch factor while keeping the max depth constant
3     10    [1.000  0.860  0.700  0.520]    77.00%
5     10    [1.000  0.940  0.840  0.720]    87.50%
8     10    [1.000  0.920  0.820  0.800]    88.50%
10    10    [1.000  1.000  0.860  0.680]    88.50%
12    10    [1.000  0.960  0.800  0.780]    88.50%
12    10    [1.000  0.940  0.800  0.760]    87.50%
15    10    [1.000  0.960  0.840  0.800]    90.00%
18    10    [1.000  0.960  0.900  0.820]    92.00%
We can see SLIGHT improvements in the quality of the results as 
we increase the branch factor.

Varying max depth while keeping the branch factor constant
5     5     [1.000  0.980  0.860  0.680]    88.00%
5     6     [1.000  0.920  0.820  0.640]    84.50%
5     7     [1.000  0.880  0.740  0.680]    82.50%
5     8     [1.000  0.940  0.740  0.520]    80.00%
5     10    [1.000  0.860  0.700  0.660]    80.50%
Results DO NOT improve with increasing depth of the vocabulary 
tree. Also, in some cases the accuracy is lost.


Now, we move to bigger sets to analyse improvements by varying the branching factor.

DATASET SIZE : 500
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5     10    [1.000  0.718  0.584  0.396]    67.45%
6     10    [1.000  0.744  0.580  0.442]    69.15%
8     10    [1.000  0.762  0.588  0.466]    70.40%
10    10    [1.000  0.776  0.630  0.454]    71.50%