How Can I combine HOG descriptors as input and CNN for Classification as output

Hi all professionals and good day,

in reference to my question, is it possible?

I have no code just yet! my process at the moment is to understand if this can be done and what are the procedures involved so that I can follow such step by step. I tried googling but I am sure that I have the wrong terminology and idea of the procedure!

Conundrum:

I am trying to extract HOG descriptor features from video sequences and feed such into rcnn for classification as the output!

Question:

1. is that possible?
2. if yes, can you point me in the right direction, please? (links , example of processes etc)

I would like to learn this on my own so that i can get the gist of the procedures involved!

what I don't know is the procedure to start the code generation process!

Please assist!

thank you in advance for your assistance and for responding to my absurd questions!

[Shams Shams]

Please if you got solutions or any information about this topic send us .

Answer 1

[Fadi Alsuhimat]

You can combine the features with dense layers. Example:

• CNN model has global average pooling before full connected layers. (512).
• Extracted hog feature vector size is 512 . You pass this feature into (512) dense layers. concatenate cnn fully connected layer with hog feature layer.
• After concatenate include one more fully connected layer and drop out. Include one more dense layer with softmax or sigmoid .

Answer 2

[Mahesh Taparia]

Hi

It seems you want to classify the image based on HOG features. You can do by storing the features in an array and pass it to a CNN. You can refer to this documentation which explain the classification of image using CNN. Moreover, you can refer to this documentation for other approach which is taking HOG features and classify the images using SVM. Hope it will helps!

[Kinjal Joshi]

How HOG features can be passed to CNN?

I want to use both features i.e. extracted by CNN and HOG features with use of concatenate layer. featureinput layer takes only dimension of features. How actual HOG features can be given to CNN?

[Sai praneeth Reddy]

brother if you got solution for this problem,please assist us !

Answer 3

[Fadi Alsuhimat]

You can combine the features with dense layers. Example:

• CNN model has global average pooling before full connected layers. (512).
• Extracted hog feature vector size is 512 . You pass this feature into (512) dense layers. concatenate cnn fully connected layer with hog feature layer.
• After concatenate include one more fully connected layer and drop out. Include one more dense layer with softmax or sigmoid .

[Shams Shams]

Good 👍 Thanks alot You mean i will use only FCL , Without using any conv.layer,or maxpool , right?

Answer 4

[Fadi Alsuhimat]

You can combine the features with dense layers. Example:

• CNN model has global average pooling before full connected layers. (512).
• Extracted hog feature vector size is 512 . You pass this feature into (512) dense layers. concatenate cnn fully connected layer with hog feature layer.
• After concatenate include one more fully connected layer and drop out. Include one more dense layer with softmax or sigmoid .