Technical Report: Exploiting Deep-Features Diversity in Food-11 Classification

This is the source-code of technical report using the Food-11 dataset. In next steps, all necessary information to perform the experiments will be presented.

Prerequisites

The experiments were built using Python 2.7.12, with some libraries: scikit-learn, theano, keras, numpy, scipy, matplotlib, Pillow and h5py. It is required to install these libraries to run the code.

• scikit-learn - Machine Learning library for Python
• Theano - Deep Learning toolkit
• Keras - High-level API for Deep learning toolkits
• numpy - Scientific computing library for Python
• scipy - Ecosystem of open-source software for mathematics, science, and engineering
• matplotlib - 2D plotting library
• Pillow - Python Image Library Fork
• h5py - HDF5 for Python

All of these libraries could be installed using pip (Python Package Index).

sudo -H pip install scikit theano keras numpy scipy matplotlib Pillow h5py

Python-Tk must be installed too, to render the GUI with python. To install it, just execute

sudo apt-get install python-tk

After installing them, we can run the experiments.

Dataset

The dataset used in our experiments is the Food-11 dataset. Food-11 contains 16643 food images grouped in 11 major food categories. The 11 categories are Bread, Dairy product, Dessert, Egg, Fried food, Meat, Noodles/Pasta, Rice, Seafood, Soup, and Vegetable/Fruit. The dataset is divided on training, validation and testing.

The total file size of the Food-11 dataset is about 1.16 GB.

Download it and put in the same directory of source-code.

AlexNet

We modified a TensorFlow version of AlexNet, found on Heuritech Github. To the feature extraction work, the ImageNet weights must be downloaded.

Put it on same directory of source-code.

Running

The experiments consists of three sequentially, but separated process:

1. Mount subsampled dataset
2. Extract features using pre-trained AlexNet
3. Choose and execute one of available experiments

The first two steps should be executed once. The third step comprises a set of experiments that can be executed as long as needed.

Mounting subsampled dataset

Just run the build_dataset.py script. In the script, folder variable chooses where to get images and max_size defines the amount of images used.

An array in numpy format (trab2_dataset.npz) will be saved on data folder.

Extracting features using pre-trained AlexNet

Just run the feature_extraction.py script. Three numpy arrays will be created on data folder (trab2_conv1.npz, trab2_conv5.npz and trab2_dense2.npz).

Running the experiments

Three experiments are available:

• Deep Features: Classification over C1, C2 and FC2 layers from AlexNet (feat_experiment.py)
• Early and Late Fusion: Classification using Early and late fusion approaches (early_fusion_experiment.py and late_fusion_experiment.py)
• Ensemble: Diversity tests using Random Forest, Majority Vote and Bagging (ensemble_experiment.py)

Authors

• Alan C. Neves
• Caio C. V. da Silva
• Jéssica Soares
• Sérgio J. de Sousa