/ObjectRecognition

Using Knn, decision tree, naive bayes, SVM and pre-trained (on imagenet data) Convolutional Neural Network for object recognition and image captioning

Primary LanguageMatlab

ORBID

Object Reognition Based Image Description

Using on pre-trained (on imagenet data) Convolutional Neural Network for object recognition

There are 2 parts to this project. Kindly follow these steps to run the code.

PART 1

This part does a comparItive study on the different classifiers and gives the testing accuracy. This is only for a single object detection.

Code file - object_recognition.m

PART 2

This part is the end to end demo of the ORBID system. Given an image, it goes through the following steps -

  1. Identify the various prominent objects in the given image
  2. Recognize the objects
  3. Identify the position of the objects relative to each other (eg., Object A is to the left of ObjectB)
  4. Build the caption using the object names and position

Code file - load_train.m, ORBID.m

Input image - Input4image.jpg