lab 4 assignment - naveenanallamotu/Big-Data-Analytics-Lab-Assignments GitHub Wiki

The project is about image classification and following are the various steps in Image classification with decision tree model.

Decision Tree model: Decision tree builds classification or regression models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed.

In Image classification, we are supposed to give to two types of images one is to build the training model and other is what we need to analyze

Training dataset -----> Key descriptors(SIFT, SURF) -----> feature vectors----> clustering(by any clustering algorithm)----> labeling of data(Histograms), test data is given to trained data model----->confusion martrix(which is used calculate the accuarcy)

Description of dataset: Trained data I have given the images of different breeds of dogs like the bull dog, labrador, yokshire, german Sheperd as my trained data.

Test data In the test data, I have given the images of dogs with human beings.

Purpose In here we try to the Identify the same breed dog (even it is different) from another set of images we given in the test data which is having dogs with human beings

Confusion matrix: A confusion matrix is a table that is often used to describe the performance of the classification model on the set of test data for which the true values are known Predicting test image : yorkshire as labdrador (2.0,3) (0.0,3) (3.0,3) (0.0,3) (0.0,3) (1.0,2) (0.0,2) (3.0,2) (0.0,2) (3.0,1) (1.0,1) (1.0,1) (0.0,1) (3.0,0) (0.0,0) (0.0,0) (1.0,0) (2.0,0) 0.2777777777777778

The confusion matrix I got from the above data. |=================== Confusion matrix ========================== 2.0 1.0 1.0 1.0
1.0 2.0 0.0 1.0
2.0 1.0 0.0 1.0
3.0 0.0 1.0 1.0
0.2777777777777778

The accuracy we got for the above data set is 0.27778 It means that Images are correctly identified in the test data by comparing the train data.