Link to source code

Team Members:

Name: Avni Mehta, Class Id: 11

Name: (Trinadha) Raji Muppala, Class Id: 14

Task 1 Objective:

Do fuzzy classification/Object classification detection three classification algorithms Random Forest, Decision Tree, Naïve Bayes models and compare the accuracy

Input: Fuzzy test features of the 3 snap shorts –front , side images of the man and mcdonald image

Code

Explaination:

For the given video and three reference objects, the video is passed through Xuggle API for object detection for the Key Frames and the training features are extracted using SIFT. Next, we run feature extraction for every test key frame where its features are extracted. Combine the features for all test key frames into one file.

The feature vector of both training and testing is passed to the image classification using a classifier (random forest and decision tree). For each testing image, the classifier predicts the weightage from all three labels.

In the end, confusion matrix and accuracy is printed.

Screenshots:

Decission Tree

Random Forest

Naive Bayes

For Naive Bayes needs positive features take absolute value of the features

Output:

Task 2 Objective:

Create the android application do the image classification/object detection using clarify Api

Input: Sunflower image from our project data set 

Code

Explaination:

Used the code from the clarify starter and the following changes made.

Output:

Task 2 problem 2:

Fuzzy image clasificaiton:

Code

Explaination:

Used the fuzzy feature Random forest, added http server code

Output:

Task 2 problem 3:

Fuzzy image clasificaiton:

Code

Code in Andriod to get the image clasification