Training images and test images

Question

I am working on a project about the feedforward pathway of the ventral stream, and i have 6 images to be recognized at the InferoTemporal Layer.

Please can someone give me images' exmamples showing to me what is the difference between training images and test images. So what i should add to my folder that contain my training images? Does i should add another folder that contain a list of test images ? if yes, what should be these test images?

Does the training images must contains the images to be analysed or recognized and the test images must contains the images in memory? In other words, if we have for example 16 training faces and one or two test faces. So we should analyse what is the face in the training that correspond to the face in test ? Is that true ??

Note: I don't need a code, I am only interested to get a brief explanations about the difference between test ans training images.

Any help will be very appreciated.

Answer 1

In HMAX, you use all the data at the input image layer. And garbor filter, max-pooling, radial basis kernel functions on all of them. Only at C2 layer, you start to train a subset of the images (mostly with a linear kernel based SVM). The subset is set to training data. And the rest are test data. In one word, training images are first used to build the SVM and then the test images are assigned to digit classes using the majority-voting method.

But this is in fact equivalent as you put the training images at the image layer at first. After all the layers going through, you then put the test images at the image layer to restart for the recognition. Since both training and test image need scaling, and all the operations at previous layers prior to C2 are the same, you just mix them altogether at the beginning.

Although you use all the training and test images at the image layer, you still need to shuffle the data and pick up some of them as the training, and the others as the testing.

Answer 2

The only difference between training and test images is the fact, that test images are not used for selecting your models parameters. Each model has some kind of paramters, variables, which it fits to the data. This is called a training process. The training/test set separation ensures, that your model (algorithm) can actually do something more that just memorizing images - so you test it on test images, which has not been used during the training phase.

It has been already discussed in detail on SO: whats is the difference between train, validation and test set, in neural networks?