Robust category recognition based on deep templates for educational mobile applications.

Popularity of mobile vision applications constantly increases. These Artificial Intelligence technology-driven solutions exhibit high potential for the dynamically-changing landscape of education, in which they could be used as powerful tools to engage and motivate learners. The integration of game-like elements into educational software can be a very effective approach to capture the attention of students by providing interactive and immersive learning experiences. That is why in this study we present a category recognition method for computationally-limited devices such as smartphones to be used in a cost-efficient manner in the areas connected to interactive education. We use small and efficient ImageNet-trained Convolutional Neural Networks and Deep Template Matching to create a classification solution. To test our solution, we construct a specialized image dataset with different toys and items that could be suitable for early-stage education to engage young learners. We perform an extensive study on how the number of images used to create templates and different similarity/distance measures applied for matching impact the accuracy to provide practical observations that can be used to create real-life game-like elements for educational software. Our results indicate that utilizing more templates and similarity measures results in better generalization, representativeness, and robustness, particularly when it comes to rotation, which is crucial for mobile applications. We release our data and code via our GitHub repository at https://github.com/iitis/EduToyz.