  We introduce and we analyze a new dataset which resembles the input to
biological vision systems much more than most previously published ones. Our
analysis leaded to several important conclusions. First, it is possible to
disambiguate over dozens of visual scenes (locations) encountered over the
course of several weeks of a human life with accuracy of over 80%, and this
opens up possibility for numerous novel vision applications, from early
detection of dementia to everyday use of wearable camera streams for automatic
reminders, and visual stream exchange. Second, our experimental results
indicate that, generative models such as Latent Dirichlet Allocation or
Counting Grids, are more suitable to such types of data, as they are more
robust to overtraining and comfortable with images at low resolution, blurred
and characterized by relatively random clutter and a mix of objects.
