Wavelets-based Feature Extraction - Part2: Wavelet Scattering Transform

This is the second part of the video that discussed the use of wavelet for feature extraction from signals and images. The focus here is on Wavelet Scattering Transform, this is a deep convolutional network that does not need to iterate to learn, but provides very informative features.

#WaveletScatter #Wavelets #Scattering #ScatteringTransform

[nabajeetb]

For those wanting to skip revision, Part 2 starts at 28:30

[MohamedAli-pvpm]

Thank you very much amazing description of the wavelet scattering transform. please keep going.

[saimaraza]

Sir its soo explain topics seem to be very dried in such an awesome manner....stay blessed always

[jb_kc__]

Top drawer explanation, really appreciate it

[miladkhazaei]

hello. it's a so good explanation. thanks👏

[bennyzhao]

Extremely excellent content, thank you for the detailed explanation!

[abhishekkumarpandey]

Thank you so much for this amazing explanation.

[-E-]

There are some errors and also mild misconceptions in this presentation, but also a lot of good in it. I definitely do not regret having spend the hour on it, thank you!

[rawadmelhem]

Thank you very much for the great explanation, but I have a question please, is there an inverse transform for Wavelet scattering? since I need to make some processing in the features extracted by wavelet scattering then go back to same domain.

[abgeenakhan]

Really very helpful and knowledge based video. Please make more videos based on EEG signal feature extraction with python implementations. Are you taking any course on any other platform?? I am really interested.

[meghaskumar]

Dr. Rami, that was really a crystal clear and crisp explanation of the topic. Sir could you please help me with the concept of multiscale pca. I have referred a lot of lectures and videos but I'm not getting that point clear. I m sure you can help me with the concept. I'm able to understand your videos very well. Thank you.

[pedrohenriqueborghi]

Hi, @AlaphBeth ty for your video! Could you clarify something? When decomposing the signal with wavelets, the decimation process won't make the left most portion of the spectrum (lowest frequencies) have less duration? If so, is it not contradictory? Should not the left most have the greatest duration due to larger wavelength? TY (:

[vinitacharya]

This is a great resource. A few months back I had to go through several videos being from an unrelated field. These videos have everything in one place. Please let me know if I can reach out to you in some way to discuss further.

[sools]

Well explained, I really wonder if you can make a video on using wavelet transform on (spectroscopy data), transferring 1D signal into an image

[filter-]

Can this technique be used for feature detection in an unsupervised manner(no training dataset, just one single data sample)? One of the wonderful things that CWT offered was pointwise discontinuity analysis (Wavelet Transform Modulus Maxima) which helped to detect interesting discontinuities from noise (based on chains built by WTMM) without any training. Is there a possibility that this technique can offer something similar to WTMM ?

[Martinko_Pcik]

9:20 I woul add that with SFFT you lose low level frequencies for capturing of which you need the longer sample time. That nicely lays out the motivation for wavelets, which addresses thei conflict between time resolution and detection of low frequencies

[emmanueldejesusvelasquezma]

hello very good video but where can I find more information on the topic of wavelet scattering?

[Aliabbashassan]

Thank u Dr. For this explanation.. I have one doubt.. Could scattering be useful jn stegoanalysis apps?

[abhishekkumarpandey]

Please make 3rd part of scattering transform.

[chayanontpotawananont]

thank you for wonderful lecture !, I have one quick question, In Multi-channel EEG problem, why there are 6 features/channel. my understanding is that each outputs from the filter are also series of coefficients. so if we use coefficient as a feature, there will result in much more features per channel