Project Idea Discussion - Image Processing

Team Meeting:

We discussed in length about the possibilities with the image processing. As mentioned in the client meeting, we wanted to get as many attributes as possible from the image.

So what we came up with was this:

1. A closed loop image subdivision algorithm based on tagging objects in images

Step 1: 

Lets assume we have a picture. any general picture. we decide to play a game of 20 questions. the player can identify and keep in his mind *any* object in the image.

Since 20 questions works on binary subdivision of possibilities, we are quickly able to reduce the item to what the player is thinking of:

1. this can be either done based on the kind of object itself. OR

2. it can be done by dividing the image into parts and asking the person if the object in his mind is in that part of the image.

Step 2: 

we are either going to guess the object in the players mind ( if we already have what the player sees in our database of metatags about this picture ) or we are going to ask the player to tag the object. once the player tags the object, we have a richer database to guess the subsequent player choices of objects.

we repeat Steps 1 and 2 in order for several players for the same image, thus resulting in a closed loop system that constantly betters itself by learning about the image from human players.

this is just a simplistic perspective and does not deal with the exact nature of game design. i am certain this can be made more exciting by using different lenses of game design. only the idea is being conveyed here. for example we could have an image like this tagged as shown.

there are so many objects that the brain sees that might take years of image recognition research to catch up to.

now objects are just nouns..

we could deal with actions that an object is doing.. verbs.. 

so we can have a reading lady in this image.. or static bike..  or walking man.

or perhaps adjectives like red light.. green light .. red bike.

so by taking a simple approach to this complex problem, we are generating huge amounts of meta data that can be highly relevant and useful for image search.

we could infact focus on one set of data.. like satellite imagery.. or something on that line.

as images become more dense ( more objects per inch ), this method of tagging becomes significant.. in fact the more complex an image is, the more fun the game is to play.

further we did consider the possibility of re-orienting an object in image to match a given plane.. so that we knew how the picture was w.r.t a normal plane.. but this is not a part of the core idea stated above.