seeking advice on HoG applicability

[Josh Warner]

Hi Lisa,

That's an interesting problem! Off the top of my head, I have a couple 
questions:

Are you making any attempt to mask the HoG features to the above-ground, 
green regions? The example images have the entire root structure shown, yet 
the visual classification you described is exclusive to the greenery. The 
roots are not well separated from the soil, either, so that entire region 
is may be confounding your task. This would be true for any feature 
algorithm, not just HoG. 

Have you tried visualizing the HoG output with the `visualize` kwarg? This 
could give you a sense for what HoG is actually extracting.

It sounds like you may have a dimensionality problem thanks to high image 
resolution, combined with a relatively low number of images to compare. 
This can be partially addressed by tweaking HoG parameters (especially 
`pixels_per_cell`, I believe) or scaling your images down to a uniform, 
smaller size. In addition, scikit-learn has several feature selection 
algorithms, such as PCA, which can help reduce the number a features to a 
manageable level.

If I get the chance to directly play with your example pictures, I'll pop 
back in with a few more thoughts.

Good luck,

Josh

On Wednesday, July 3, 2013 2:10:19 PM UTC-5, Lisa Torrey wrote:
>
> 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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