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