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 .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> 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

Classification For the list of test images, run SVM-KN python classify.py This process creates a classlabel file called test.clslbl which contains image_name SVM-KNN_category

Error Evaluation python evaluation.py This file creates a file containing confusion matrix called test.cnfmtx and a correctness_rate_graph.png