Home // ICSEA 2019, The Fourteenth International Conference on Software Engineering Advances // View article
Authors:
Hirokazu Madokoro
Hanwool Woo
Kazuhito Sato
Keywords: bags of features; category maps; convolutional neural networks; counter propagation networks; self-organizing maps; and semantic indoor scene recognition.
Abstract:
Human living indoor environments are changing continuously according to our various lifestyles and activities. Human-symbiotic robots require advanced capabilities of environmental understanding and adaptation. For robotic environmental adaptation, numerous machine-learning-based approaches have been proposed. Moreover, numerous types of features such as brightness, edges, texture, etc. have been used for learning networks. This study was conducted to evaluate combinations of supervised-learning-based indoor scene recognition methods and their input features. This paper presents a framework to provide image features of three types according to learning strategies. The experimentally obtained results evaluate using two open benchmark datasets revealed suitable combinations of input features including weights obtained from category maps of Counter Propagation Networks (CNNs) used for Deep Neural Networks (DNNs). We demonstrate a suitable combination of features from scene images used for semantic indoor scene recognition. Particularly, higher recognition accuracy is obtainable using original time-series images for learning with CNNs.
Pages: 190 to 195
Copyright: Copyright (c) IARIA, 2019
Publication date: November 24, 2019
Published in: conference
ISSN: 2308-4235
ISBN: 978-1-61208-752-8
Location: Valencia, Spain
Dates: from November 24, 2019 to November 28, 2019