Reducing response time for multimedia event processing using domain adaptation

Abstract

The Internet of Multimedia Things (IoMT) is an emerging concept due to the large amount of multimedia data produced by sensing devices. Existing event-based systems mainly focus on scalar data, and multimedia event-based solutions are domain-specific. Multiple applications may require handling of numerous known/unknown concepts which may belong to the same/different domains with an unbounded vocabulary. Although deep neural network-based techniques are effective for image recognition, the limitation of having to train classifiers for unseen concepts will lead to an increase in the overall response-time for users. Since it is not practical to have all trained classifiers available, it is necessary to address the problem of training of classifiers on demand for unbounded vocabulary. By exploiting transfer learning based techniques, evaluations showed that the proposed framework can answer within ∼0.01 min to ∼30 min of response-time with accuracy ranges from 95.14% to 98.53%, even when all subscriptions are new/unknown.