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I'm trying to score video scenes in terms of aesthetics and cinematography features. Basically, how "interesting" a scene or video frame can be for a viewer. Simpler, how attractive a scene is. My final goal is to tag intervals of video which can be more interesting to viewers. It can be a "temporal attention" model as well.

Do we have an available model or prototype to score cinematographic features of an image or a video? I need a starter tutorial on that. Basically, a ready-to-use prototype/model that I can test as opposed to a paper that I need to implement myself. Paper is fine as long as the code is open-source. I'm new and can't yet write a code given a paper.

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  • $\begingroup$ Generally we don't just find and link tutorials here. Could you re-phrase the question so that it is more about the problem you face. Any initial thoughts or partial solutions you have would help too, as it helps to pitch the answer correctly for where you are technically. For instance, how familiar are you with CNNs used for image classifying and regression? Have you tried to collect a bunch of images with subjective scores and train a regression model to predict subjective score on new images - or failing that have you looked for such a model? $\endgroup$ – Neil Slater Aug 15 at 22:03
  • $\begingroup$ I'm new to CNN/DL. I couldn't find an available tool. Was hoping to get some useful insights here. $\endgroup$ – Tina J Aug 15 at 22:31
  • $\begingroup$ OK, I think the question needs something more to go on that looking for a "starter tutorial" and "get some useful insights". Could you add a little more detail to the question about what you know already from this topic, and what kind of model you are looking for? For instance if someone was to find a mathematical paper discussing the maths of loss functions and complexity of the problem (Google published one or two in this area, amongst others), would it be any use to you? Or are you really looking for some Python code that you can hack? Use edit to add details. $\endgroup$ – Neil Slater Aug 16 at 8:43
  • $\begingroup$ Mode ready-to-use prototype/model as opposed to a paper that I need to implement myself. I'm new and can't yet write a code given a paper. $\endgroup$ – Tina J Aug 16 at 15:54
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    $\begingroup$ You should check the repo's license for the repo you want to use, and check whether use of NIMA is encumbered by any patents. The Apache 2 license protects you against copyright and patent claims by the publisher of the repo, so that's a good start. Also, a lot of ML published methods are fine to use in practice, it is a relatively open environment (unlike some areas of research). I would say you are very probably OK to go ahead, but I am not a lawyer - at some point you will want to do legal due dilligence. Personally I would add that to the list of things to check for the startup. Good luck $\endgroup$ – Neil Slater Aug 17 at 6:16
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Aesthetics of images has a strong subjective element and possibility of multiple dimensions depending on purpose of the media. That means:

  • It is hard to define what we mean by scoring aesthetics.

  • Given any well-constrained definition, it is then time-consuming to collect relevant data.

However, there is some interest in the machine-learning community, as media quality would be a very useful metric to sort and filter data on (provided the metric is close enough to the end user who wants to select it). As a result, there are data sets, research papers and pre-built models for this.

Media quality training data can be crowdsourced in a variety of ways, including looking at popularity of items on social media, to paying experts to assess large numbers of images. An example of one open dataset compiled by researchers for this purpose is called AVA.

This data might be reduced to image/quality pairs which you can then train a CNN model to predict the quality metric (score out of 10 for example). This might just be a regression, but other more complex loss functions are also considered.

A quick search for existing models brings up Google's NIMA project, which has more than one implementation available as open-source code. NIMA appears to use multiclass classification approach to predict which ratings humans would most likely give the image, and the resulting score is then a weighted average of the predicted scores - the claimed benefit of that seems to be that it better matches how the quality ratings are sourced, and it will better capture split opinions (e.g. half of people think the image is terrible, but half think it is great is a different type of image to one where everyone thinks it is just average).

Here is an implementation of NIMA by Github account "idealo" looks complete with documentation, and ready to use with pre-built scripts.

Just to show this is not a one-off, here's a blog by Andrej Karpathy about using CNNs to rate selfies which includes some introduction to core CNN concepts.

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  • $\begingroup$ Aesthetically pleasing can vary from region to region....And is also non-stationary with time....Albeit I think it depends on a huge amount of factor when something will become aesthetically pleasing. I am not sure but I think Dawkins first put forth the idea of memes as a kind of cultural tool which propagates/popularises an idea. I don't think ML can handle all these factors. $\endgroup$ – DuttaA Aug 16 at 17:43
  • $\begingroup$ Thanks. Yes I actually found NIMA yesterday, and the pre-trained models are really helping me. Although it's more of a quality assessment (and not attention model), but good enough to start with. It's photo-based (and not video), but still applicable enough. $\endgroup$ – Tina J Aug 16 at 19:18
  • $\begingroup$ Your inputs are welcome: ai.stackexchange.com/questions/14153/… $\endgroup$ – Tina J Aug 27 at 3:40

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