Well, you are trying so describe the image by predominant line directions and that really sounds fair but a bit of too much work. If you want to you can try a bit of data augmentation to increase your numbers of samples (Mainly affine transformations)
Other possible useful feature would be descriptors such as:
- SIFT
- SURF
- FAST
- BRIEF
You could try to describe those shapes using other geometrical functions such as skewness (fill the inside of the shape, consider the eigenvalues to define the major axis directions on PCA)
Finally, bottles in the sample image look similar in shape and differ mainly in size, if your model uses resize it might become scale invariant and then your method will decrease too much of your shape information and give bad results.
##------------------------------------------------------------------------------------
Answering the comment bellow:
This is why i tried to do it with my approach of cropping out each bottle and split them into a 2d array of tiles (e.g. 50x20)
This inserts a scale invariance to your model, that is good for processes where image-size vary either because of distance or camera resolution but: This throws alway scale information, that might not be helpful when the main discriminant on your image is scale-related.