# MirBot: A collaborative object recognition system for smartphones using convolutional neural networks

## TL;DR

MirBot is a smartphone app that uses CNN-based similarity search for object recognition, continuously improving through user feedback and a growing multimodal dataset, enabling research and maintaining accuracy over time.

## Contribution

This work presents a novel collaborative object recognition system leveraging CNN features and user feedback to enhance accuracy and dataset growth over four years.

## Key findings

- CNN features maintain accuracy over time despite new classes
- The dataset is multimodal and incrementally growing
- System performance evaluated with various CNN models

## Abstract

MirBot is a collaborative application for smartphones that allows users to perform object recognition. This app can be used to take a photograph of an object, select the region of interest and obtain the most likely class (dog, chair, etc.) by means of similarity search using features extracted from a convolutional neural network (CNN). The answers provided by the system can be validated by the user so as to improve the results for future queries. All the images are stored together with a series of metadata, thus enabling a multimodal incremental dataset labeled with synset identifiers from the WordNet ontology. This dataset grows continuously thanks to the users' feedback, and is publicly available for research. This work details the MirBot object recognition system, analyzes the statistics gathered after more than four years of usage, describes the image classification methodology, and performs an exhaustive evaluation using handcrafted features, convolutional neural codes and different transfer learning techniques. After comparing various models and transformation methods, the results show that the CNN features maintain the accuracy of MirBot constant over time, despite the increasing number of new classes. The app is freely available at the Apple and Google Play stores.