Robert Johnston wrote:
What I'm suggesting is that rather than having to keep adding bits to the commercial flagging routines every time the broadcasters change their tack, which ties up development time that could be used on other things, we have a "Learning" system that will adapt "on the box" to improve commercial detection in a continuing and ongoing way, without having to rewrite, rebuild, or recompile.
It looks like you have a very skewed idea of the development and testing time required for such a system.
This is what I'm talking about. I'm suggesting using a "Learning" algorithm for commercial flagging to work from, which will learn and constantly improve itself based on user's input, tailoring itself to their viewing preferences and wants.
algorithms don't "just learn". You need to provide it with sensible input. Like the amount of camera switches, wether or not there is a logo, sound levels etc... Just like the current system needs those input. Neural nets or baysian filters aren't magical. They need the same inputs as the current system, and I'd bet both nets and baysian filters will be greatly outperformed by a handmade ruleset.
the problem is getting the the input parameters. once the detection of those works better, commercial flagging gets much better. while it doesn't work, there ain't no funky learningalgorithm thats suddenly going to work well.
I agree that we need more eyes on this area, more people helping out. I'm just offering a suggestion as to how we can "Solve" the commercial flagging problem once and for all.
Sure thing. I'm just trying to make sure the people who might be listening and thinking "hey, I could help with that" realize that there are other areas that need help that have a much better value/timespent ratio.
Bye, Lucas _______________________________________________ mythtv-dev mailing list [email protected] http://mythtv.org/cgi-bin/mailman/listinfo/mythtv-dev
