Resource type
Date created
2019-01-30
Authors/Contributors
Author: Othman, Kamal M.
Author: Rad, Ahmad B.
Abstract
The ability to classify rooms in a home is one of many attributes that are desired for social robots. In this paper, we address the problem of indoor room classification via several convolutional neural network (CNN) architectures, i.e., VGG16, VGG19, & Inception V3. The main objective is to recognize five indoor classes (bathroom, bedroom, dining room, kitchen, and living room) from a Places dataset. We considered 11600 images per class and subsequently fine-tuned the networks. The simulation studies suggest that cleaning the disparate data produced much better results in all the examined CNN architectures. We report that VGG16 & VGG19 fine-tuned models with training on all layers produced the best validation accuracy, with 93.29% and 93.61% on clean data, respectively. We also propose and examine a combination model of CNN and a multi-binary classifier referred to as error correcting output code (ECOC) with the clean data. The highest validation accuracy of 15 binary classifiers reached up to 98.5%, where the average of all classifiers was 95.37%. CNN and CNN-ECOC, and an alternative form called CNN-ECOC Regression, were evaluated in real-time implementation on a NAO humanoid robot. The results show the superiority of the combination model of CNN and ECOC over the conventional CNN. The implications and the challenges of real-time experiments are also discussed in the paper.
Document
Published as
Othman KM, Rad AB. An Indoor Room Classification System for Social Robots via Integration of CNN and ECOC. Applied Sciences. 2019; 9(3):470. DOI: 10.3390/app9030470
Publication details
Publication title
Applied Sciences
Document title
An Indoor Room Classification System for Social Robots via Integration of CNN and ECOC
Date
2019
Volume
9
Issue
3
Publisher DOI
10.3390/app9030470
Rights (standard)
Copyright statement
Copyright is held by the author(s).
Scholarly level
Peer reviewed?
Yes
Language
English
Member of collection
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