[SOLVED] ComputerVision- Homework 5

Description

• Tasks:
  1. Tiny images representation + nearest neighbor classifier 
(accuracy of about 18-25%)
  2. Bag of SIFT representation + nearest neighbor classifier

(accuracy of about 50-60%)

3. Bag of SIFT representation + linear SVM classifier

(accuracy of about 60-70%)

Extra bonus: try to use deep learning! (you can choose any type of neural network model)

• You need to evaluate the accuracy of your model.
• You can use
http://www.vlfeat.org/download.html
http://www.vlfeat.org/matlab/matlab.html

Goal: builds a classifier to categorize images into one of 15 scene types!

1. Tiny images representation + nearest neighbor classifier

Tiny images representation

• Simply resizes each image to a small, fixed resolution (16*16).
• You can either resize the images to square while ignoring their aspect ratio or you can crop the center square portion out of each image.
• The entire image is just a vector of 16*16 = 256 dimensions.
• You can use functions (MATLAB): imread, imresize

1. Tiny images representation + nearest neighbor classifier

Nearest neighbor classifier

• Instead of 1 nearest neighbor, you can vote based on k nearest neighbors which will increase performance (although you need to pick a reasonable value for k).

Training example Test examples Training

examples from class 2

from class 1

f(x) = label of the training example nearest to x

• All we need is a distance function for our inputs
• No training required!

2. Bag of SIFT representation + nearest neighbor classifier

Bag of SIFT representation

2         0         1 ……

Histogram

2. Bag of SIFT representation + nearest neighbor classifier

Bag of SIFT representation

SVM

• Find!a!linear’func+on’to!separate!the!classes:!

!f(x)!=!sgn(w(⋅!x(+!b)!

• You can use functions (MATLAB): fitcsvm, predict

Example: cat facial recognition    Training Phase

SVM model

9

Example: cat facial recognition  Detection Phase

10

Extra bonus: deep learning

Example: Convolutional Neural Network (CNN)