Welcome to Exploring Technologies

A True Learning Platform

Have smile on face and Confidence in heart

Learn with me, Grow with Me

Become the leader and step ahead of others

Do excel in areas of your expertise and lead the world

Come and join hands with me

Let us learn and grow together to make our tomorrow better.

Video 46: Object Classification using HOG features and ECOC Multi-Class SVM (With Matlab Code)

Hello Viewers, in this video, a multi-class object classification problem using HOG features is explained. To demonstrate the implementation, simple geometrical shapes (Circle, Square, Star and Triangle) are taken for classification. As a classifier, ECOC (Error Correcting Output Codes) based multi-class SVM is used. The shapes image database is obtained from Kaggle. 

The HOG feature is very popular and widely used for object detection in images. To understand the HOG feature computation, viewers are requested to watch my previous video of HOG feature computation.

This video includes following contents:

  • Introduction.
  • Proposed scheme for object Classification.
  • Image Database Preparation.
  • ECOC based Multi-Class SVM.
  • Appropriate Cell Size selection for HOG feature.
  • MATLAB Code for Shapes Classification (Multi-Class).
  • MATLAB Code for Discrete Testing.

1. Link for previous video on HOG feature computation: Click Here

2. Link for Kaggle Dataset: https://www.kaggle.com/smeschke/four-shapes

3. Link to download original paper of N. Dalal and Bill Triggs:


Download Resources: 

1. Image Dataset (Modified): Download (Source: Kaggle)

2. Distorted Test Images: Download