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Some ideas for enhancing your BoW-performance:
Your BoW-Descriptors probably look too similar. Have you incorporated some kind of locality? E.g. by computing 4 BoW-features in each square of the image? With the pure BoW approach you'll lose the locality of features. More ideas on how to improve BoW can be found in these pretty good slides: http://people.rennes.inria.fr/Herve.Jegou/courses/2012_cpvr_tutorial/3-bagofwords.ppt.pdf
Maybe your features are not discriminative enough. You could maybe combine different features like SIFT+LBP like in: http://icmll.buaa.edu.cn/members/jing.yu/YuanYuQinWan.pdf . My idea would be to try out just HOG features since they are directly related to the orientation in which you are interested in.
Which classifier did you use? Try different ones (if you are lazy to adjust them using OpenCV you could use WEKA or python-scipy just for the classification task)!
Maybe you could detect something else which could be discriminating your two classes, e.g. try to detect the eyebrows of the horses in the image and see if they are closed, or other similarities/ dissimilarities in your images (which is from these 5 images hard to tell). You could also try to detect the shapes and make a kinda shape matching.
