Algorithm for clustering pictures based on date taken

Question

Anyone know of an algorithm that will group pictures into events based on the date the picture was taken. Obviously I can group by the date, but I'd like something a little more sophisticated that would(might) be able to group pictures spanning multiple days based on the frequency over a certain timespan. Consider the following groupings:

• 1/2/2009 15 photos
• 1/3/2009 20 photos
• 1/4/2009 13 photos
• 1/5/2009 19 photos
• 1/15/2009 5 photos

Potentially these would be grouped into two groups:

1. 1/2/2009 -> 1/5/2009
2. 1/15/2009

Obviously there will be some tolerance(s) that need to be established.

Is there any well established way of doing this, other then inventing my own top/down approach?

Answer 1

You can apply pretty much any standard clustering technique to this, it's just a matter of defining your distance function correctly. When you are making your matrix of distances between your photos you should consider a combination of physical distance between locations - if you have it - and temporal distance between their creation timestamps. Normalise them and put them on separate dimensions and you may even just be able to take a regular euclidean distance.

Best of luck.

Answer 2

Just group the pictures that were taken on successive days (no days on which no pictures were taken) together.

Answer 3

You might try to dynamically calculate tolerance based on how many or how big (absolute or %) clusters you want to create.

Answer 4

To get a useful clustering of pictures according to date you require the following:

1) The number of clusters should be variable and not fixed a priori to the clustering

2) The diameter of each cluster should not exceed a specific amount.

The clustering algorithm that best satisfies both requirements is the QT (quality threshold) clustering algorithm. From Wikipedia:

QT (quality threshold) clustering (Heyer, Kruglyak, Yooseph, 1999) is an alternative method of partitioning data, invented for gene clustering. It requires more computing power than k-means, but does not require specifying the number of clusters a priori, and always returns the same result when run several times.

Although it is mainly used for gene clustering I think it would fit in very well for what you need.

Answer 5

Try to detect the Gaps instead of the Clusters.