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Foodcam Classifier
==================
A Bag Of Visual Words classifier for MIT Media Lab's Foodcam.
Built using OpenCV 2.3

Implementing BOVW method: Visual Categorization with Bags of Keypoints by Gabriella Csurka, Christopher R. Dance, Lixin Fan, Jutta Willamowski, Cedric Bray, 2004.
Using Opponent-Color SURF features (192 floats): Van de Sande et al., CGIV 2008 Color Descriptors for Object Category Recognition. (they used SIFT)
Radial Basis Function SVMs for classification.

This is basically a primer to BOVW methods using OpenCV, which are dead simple.
I tweaked the basic method by:
	- using background subtraction (since these are pictures from a still camera).
	- using a sliding window. This enables better multi-class decisions, and actually paves the path to image segmentation. 


Compiling
---------
Basically, just run:
cmake -D CMAKE_CXX_FLAGS=-fopenmp . ; make -j4

CMake should take you all the way, but I also have an .xcodeproj/ included.

Working it
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Get the dataset:
http://fay.media.mit.edu/foodcamimages.zip
Assume the dataset is now in the same directory, under foodcamimages/.


Manually classify training and test:
./manual-classifier foodcamimages/TRAIN/ train.txt
./manual-classifier foodcamimages/TEST/ test.txt
(or you can use my manual classification included... lazy)


Create a background image:
./make-test-background


Build the vocabulary: (this will take a very very long time, KMeans on 1.3Million 192-long vectors to find 1000 cluster-centers...)
./build-vocabulary

(you may also like to use ./kmeans-trainer, if you break the operation before KMeans finished, which may take more than 10 hours)


Train the classifiers:
./train-bovw vocabulary_color_1000.yml with_color

(you may also make use of './train-SVM-alone', if you just wanna tweak the SVM parameters)


Test your work:
./test-classifiers 
(it will output a whole lotta things, and then the confusion matrix in neighbors list form)


Put it to work!:
./foodcam-predict some_640x480_image_of_food.png
Will output either one or two found classes of food, based on how close the prediction is.


Notes
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Some of the computation is sped up on multi-core machines by using OpenMP, so it's recommended to use it.


Results
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This is the confusion matrix:
classified ->	cookies	indian	italian	pizza	veggie+fruit	sandwiches
cookies		56.3%	0.0%	4.3%	0.0%	13.5%		15.4%
indian		0.0%	71.4%	6.4%	3.8%	3.4%		7.7%
italian		0.0%	0.0%	63.8%	0.0%	14.6%		0.0%
pizza		6.3%	0.0%	12.8%	92.3%	5.6%		15.4%
veggie+fruit	0.0%	0.0%	2.1%	3.8%	49.4%		0.0%
sandwiches	25.0%	14.3%	6.4%	0.0%	7.9%		61.5%

Quick scan shows that it can classify Pizzas pretty good!
The downfall are the veggie+fruit class, with less than 50% accuracy.
Rest are lukewarm, but this can probably be attribued to the very small training set.