Classification

Developed the program using Convoluional Neural Networks for parking space classification and counting occupied parking spaces, whose accuracy reached 90%, precision was 96%. We incorporated the mutual information method for image registration and the affine transformation to eliminate the impact caused by camera shake, the robustness of detection was enhanced.

Re-implemented the hierarchy based classification and embedding method for encoding of categories to decrease average hierarchical distance at top 1 by 3%, and that at top 5 by 10% on our [VIPER-FoodNet dataset](https://lorenz.ecn.purdue.edu/~vfn/) with 82 food categories, 15 thousand images.