The given code implements geometric blur descriptor and performs SVM-KNN on Caltech 101. 

1)Description of Geometric Blur descriptor can be found in 
"Geometric Blur for template matching", Alex Berg, Jitendra Malik, CVPR 2001
2)Description of SVM-KNN can be found in 
"SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition", 
H. Zhang, A. C. Berg, M. Maire, J. Malik. CVPR 2006 
3)L. Fei-Fei, R. Fergus and P. Perona. Learning generative visual models
from few training examples: an incremental Bayesian approach tested on
101 object categories. IEEE. CVPR 2004, Workshop on Generative-Model
Based Vision. 2004 (Mentioned here for citation purpose)
4)David G. Lowe, "Distinctive image features from scale-invariant keypoints," 
International Journal of Computer Vision, 60, 2 (2004), pp. 91-110

To run this code you need:
--Caltech 101 dataset
--Python libraries such as PIL, NumPy, SciPy, PyML 
--You also need David Lowe's SIFT code for keypoint detection. 
(Other keypoint detection schemes could also have been used. 
SIFT was used for easy code availability and integration)

List of Files included:
create_train_test.py : This files creates list of training and testing files
create_descriptor.py : This file creates geometric blur descriptor for given images 
and saves them in <filename>.decp file
utility_functions.py: This file contains all the utility functions used in this entire
code directory
gb_params.py: This file contains some constants used in utility_functions.py
classify.py: This file implements SVM-KNN from based on geometric blur descriptor for caltech 101


Following is the brief description of the idea of the code

Geometric Blur part:
keypoint detection -  SIFT code of David Lowe
4 channels are used overall. Each channel is a sobel filter at various orientation 
  (various other boundary detection techniques could have been used)
4 blurs are used at varying standard deviation
24 points are sampled around the keypoint are sampled at 4 radii
Total no of points in the descriptor = 24 samples * 4 radii * 4 channels 
= (x,y) +  384 points per keypoint  = 386 points
Distance measure in Algorithm B is implemented from SVM-KNN 
paper given in Section 4.4 for distance measure

SVM-KNN part:
15 training samples from 101 categories are chosen at random from Caltech 101
Hence we have 15*101 = 1515 training images
Remaining images are used as testing images

For each testing sample:
    Calculate the distance between testing image and all the training image
    Choose K nearest neighbors (we are considering K = 30 for our experiments)
    if all K neighbours have same class label
       the given test image has the same class
    else 
    	 perform multi-class SVM based on K-nearest neighbours and find class label for given test image

Perform error analysis for given iteration

This method can be carried out number of times for different sets of training images
Perform more iterations if needed

Code:

Example of Use:
Get Caltech 101 dataset and untar it

Create training and testing sets
python create_train_test.py <path of caltech 101 folder> <no of training images per object category> 
This program creates
     train.lst =  list of training images
     test.lst = list of testing images

Compute geometric blur descriptor for list of training images around points defined by SIFT keypoint detection
Get geometric blur descriptor around all the points for list of training images 
and save them in the same directory as the respective image
python create_descripto0r.py <list of training images> <path of SIFT binary of David Lowe>

Classification
For the list of test images, run SVM-KN
python classify.py <path of test.lst> <path of train.lst> <path of SIFT binary of David Lowe>
This process creates a classlabel file called test.clslbl which contains image_name SVM-KNN_category

Error Evaluation
python evaluation.py <path of test.clslbl>
This file creates a file containing confusion matrix called test.cnfmtx and a correctness_rate_graph.png