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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:
Potentially these would be grouped into two groups:
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?
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The most common approach to time series clustering is to flatten the time series into a table, with a column for each time index (or aggregation of the series) and directly apply standard clustering algorithms like k-means.
For time series clustering with R, the first step is to work out an appropriate distance/similarity metric, and then, at the second step, use existing clustering techniques, such as k-means, hierarchical clustering, density-based clustering or subspace clustering, to find clustering structures.
It is a method to perform Image Segmentation of pixel-wise segmentation. In this type of segmentation, we try to cluster the pixels that are together. There are two approaches for performing the Segmentation by clustering. Clustering by Merging. Clustering by Divisive.
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.
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