This is best solved with machine learning.

You're going to spend far more time on writing a program than actually just doing it yourself in this situation.

From my experience, that is very much true for outsourcing the job, too. You first spend 2 hours writing a process document, or alternatively you spend 2 hours to train that person (for something you could have done yourself in 2 hours, too). Then the person says that they've understood the task, and a week later you get back something which isn't necessarily what you asked for. So you pick up the phone for a "WTF is this shit?" call, and waste another 15 minutes to find out that they hadn't understood what you were asking but said "yes, yes" anyway. So you explain it again, hoping they do it right this time. And if you're particularly unlucky, their supervisor hears about it, and asks for another call to discuss the issue, for customer satisfaction, you know. Which means you waste another hour for nothing.

But back to topic: Finding all images with "One Piece" in them sounds like it's maybe a simple special case of a very complicated problem (e.g. photos from collectible cards or such, which are all somewhat similarly colored (cartoon image!) and all have one somewhat identical region).

In that case, you can probably get away with first pruning everything that's obviously not what you want using a histogram, and then matching sub-regions of a very downsampled version of the image. The logo area will have the same averaged color (or very nearly so) in every photo if it's for example downsampled 16 or 32 times (or more), even if there are some variations.

Such as for example in this One Piece photo that I stole from the internet, the "mostly black" area with the "mostly blue and some black" logo will result in two black squares left and right of a dark blue square:

(seems like the gd.net server doesn't like me posting images directly, so here it is...)