Is there a crucial distinction between semantic and just normal image segmentation with neural networks? Is non-semantic segmentation some type of unsupervised pixel-clustering method?
If you take a look to W-net, you can see that you can do unsupervised segmentation with deep learning.
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I am clear with the tasks of instance segmentation and semantic segmentation. However, from the perspective of the neural networks, what is the relationship between them? Namely, is it feasible to realize instance segmentation by improving or modifying a neural network for semantic segmentation, e.g. DeepLab? If so, what operations are usually used? Many thanks.
Lets assume that you want to know where exist desired class in image then you makes nn that for each pixel predict probability where is that class - this is sematic segmentation.
And when you want to know where exist each instance for desired class this is instance segmentation.picture for example
I'm doing a language recognizer, I had planned to classify my i-vectors with neural networks, but I've read a lot of papers and they always use other methods like SVM or PLDA, can someone explain to me why? or it's fine to do it with neural networks?
Neural networks are good for complex non-linear multifeature input. I-vectors by design map speaker space to very simple space where speakers are easily separated with logistic regression or SVM.
If you want to try with neural networks, try something end-to-end like https://github.com/FlashTek/vggvox-pytorch
I've tried to follow the example provided at mathworks for training a deep sparse autoencoder (4 layers), so i pre-trained the autoencoders separately and then stacked then into a deep network. When i try to finetune this network though, via the
train(deepnet, InputDataset)
instruction, the training stops instantly and i receive a "performance goals met" message.
Is there a way to train and finetune a deep autoencoder network in an unsupervised manner in Matlab (no labels provided)?
Have you set the "MSE" goal? Secondly, for fine tuning of network use a conventional back-propagation algorithm in supervised fashion.
I am beginner in deep learning.I am using deep neural network [DNN] for image segmentation. I have few doubts. I have input image size 512x512.1. I want to select 6 Kernels of 5X5 pixels.I could not understand these kernels how I have to select, is there any standard kernel available? if yes please tell me.2. How can I take patch of a image? is it like manual cropping of some part from original image?
A very good paper for CNN-based segmentation is "Fully Convolutional Networks for Semantic Segmentation" by J. Ling et al. and they released their pre-trained networks. They can be found in the Caffe model zoo page. They also released their code (in Caffe), so it is possible to train or fine-tune models on new segmentation problems.
Note that these models directly learn the "complete" segmentation of the images. They do not rely on sampled image patches with a single class as output like previous classification-based approaches.
I am trying to code an OCR for shop tickets (in Java), I have good results with image dictionary distance, but not for skewed texts or bad scans.
I heard that neuronal networks are perfect for this.
Question: which type of neuronal network do you recommand for shop tickets character detection ?
Thks
Neural networks will not magically solve the problem for you. They will have similar problems that your current approach has. Most likely you will have to detect skew and correct it before sending it to a classifier.
Similarly with bad scans. It depends what exactly a bad scan is. For example, some neural networks are amazingly efficient at correcting blurs (unfocused image, blur by move, ...).
Have a look at some papers about OCR and neural networks. It is a classical topic so there are many. For example The Anatomy of Bangla OCR System for Printed Texts Using Back Propagation Neural Network also tries to solve the problem of skewed images before running a neural network.
I know that recurrent neural networks can be used for OCR. Even a very simple one will easily recognize simple characters. There is a recent paper that improves upon them: High-Performance OCR forPrinted English and Fraktur using LSTM Networks. They even include text-line normalization which may be very useful in your case.
Notice that there is an answer here about training a normal Feed-forward backpropagation neural network for OCR too: training feedforward neural network for OCR
"Convolutional Neural Networks" with "Deep Learning" have been shown to give some of the best results in OCR (specifically on the MNIST database).
A good starting point is this tutorial.