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A Bag of Visual Words food classifier for Media Lab's FoodCam
This project forked from royshil/foodcamclassifier
A Bag of Visual Words food classifier for Media Lab's FoodCam
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 ---------- Get the dataset: http://www.media.mit.edu/~roys/shared/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 ----- Some of the computation is sped up on multi-core machines by using OpenMP, so it's recommended to use it. Results ------- 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.
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