seeking advice on HoG applicability
Lisa Torrey
lisa.torrey at gmail.com
Wed Jul 3 15:10:19 EDT 2013
Hi folks -
I'm looking at an image classification problem, and wondering whether HoG
should be applicable to it. If any of you are willing to take a look and
give some advice, that would be wonderful. I have a machine learning
background, but working with image data is a new area for me.
The problem is to distinguish between two types of moss. Type 1 tends to
consist of upright stalks with few or no branches. Type 2 tends to have
secondary branches coming off the primary stalk. There's quite a bit of
visual diversity within these types. I've linked some images below.
Type 1:
http://myslu.stlawu.edu/~ltorrey/moss/andrea_rothii.jpg
http://myslu.stlawu.edu/~ltorrey/moss/mnium_spinulosum.jpg
Type 2:
http://myslu.stlawu.edu/~ltorrey/moss/climacium_americanum.jpg
http://myslu.stlawu.edu/~ltorrey/moss/rhytidiadelphus_triquetrus.jpg
When I came across the Dalal paper, I thought my problem might have
something in common with the pedestrian detection problem, so I tried
extracting HoG features and feeding them into an SVM classifier. This
failed miserably - the SVM does no better than random guessing. I'm now
trying to weigh potential reasons.
The first possible reason on my list is the diversity among mosses of the
same type. There isn't necessarily a "type 1 shape" and a "type 2 shape,"
at least not to the degree that there's a "pedestrian shape." Perhaps this
means HoG isn't really the right approach to my problem after all?
Other reasons may include:
- I have much less data. (Just 77 positives and 78 negatives, compared to
Dalal's 1239 and 12180.)
- My images aren't all the same size, like the pedestrian images are. (I'm
not sure if this would matter?)
- My images are much higher resolution. (I've been downscaling them by a
factor of 8, but the feature vectors are still enormous.)
- I'm just using default parameters so far. (In the absence of any signal,
tweaking seems unproductive.)
Any thoughts or suggestions would be welcome!
-Lisa
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