Personalized object recognition for augmenting human memory
| Autor: | Cameron Upright, Hosub Lee, Steven Eliuk, Alfred Kobsa |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
business.industry
Computer science Deep learning Cognitive neuroscience of visual object recognition Wearable computer 020207 software engineering 02 engineering and technology Convolutional neural network Personalization Task (computing) Human–computer interaction 020204 information systems 0202 electrical engineering electronic engineering information engineering Artificial intelligence business Auxiliary memory |
| Zdroj: | UbiComp Adjunct |
| DOI: | 10.1145/2968219.2968568 |
| Popis: | We propose a novel wearable system that enables users to create their own object recognition system with minimal effort and utilize it to augment their memory. A client running on Google Glass collects images of objects a user is interested in, and sends them to the server with a request for a machine learning task: training or classification. The server processes the request and returns the result to Google Glass. During training, the server not only aims to build machine learning models with user generated image data, but also to update the models whenever new data is added by the user. Preliminary experimental results show that our system DeepEye is able to train the custom machine learning models in an efficient manner and to classify an image into one of 10 different user-defined categories with 97% accuracy. We also describe challenges and opportunities for the proposed system as an external memory extension aid for end users. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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