Ideas for separating LDA clusters to improve SVM accuracy?

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I've been trying to classify some tags (each tag is a 24*24 pixel image) that I have on the backs of insects. There are 3 tag types: circle (O), rectangle (I) and Queen (Q). There's also a fourth tag type: unknown (U) because sometimes the angle that the tags are extracted at or the lighting mean that it isn't possible to tell what type of tag it is. There's quite a bit of variation in lighting and tag quality too, which you can see in the image below (image 7.png is actually a rectangle that's a little bit blurry although in the image I said it was unknown):

I've tried to use PCA and LDA on this data, LDA initially looked promising when I only trained it with easily lit tags, however once I include all tag types, the LDA looks a little less clear:

If I apply histogram equalisation to the tags, the queen tag seems to separate out reasonably well, and if I train an SVM with two classes (queen vs all others) it's 95% accurate.

I could train two SVMs where the first extracts the queen and then I use the other one to separate out the circles and rectangles. The problem is that there seems to be a fair bit of overlap between the LDA distributions of these shapes. Does anyone have any advice on other image processing, dimensionality reduction or machine learning techniques that I could try to separate these two groups?