Modified Capsule Neural Network (Mod-CapsNet) for indoor home scene recognition

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In this paper, a Modified Capsule Neural Network (Mod-CapsNet) with a pooling layer but without the squash function is used for recognition of indoor home scenes which are represented in grayscale. This Mod-CapsNet produced an accuracy of 70% compared to the 17.2% accuracy produced by a standard CapsNet. Since there is a lack of larger datasets related to indoor home scenes, to obtain better accuracy with smaller datasets is also one of the important aims in the paper. The number of images used for training and testing is 20,000 and 5000 respectively, all of dimension 128X128. The analysis proves that in the indoor home scene recognition task the combination of the capsule without a squash function and with max-pooling layers works better than by using capsules with convolutional layers. Indoor home scenes are specifically focused towards analysing capsules performance on datasets whose images have similarities but are, nonetheless, quite different. For example, tables may be present in living rooms and dining rooms even though these are quite different rooms.
Original languageEnglish
Number of pages6
Publication statusPublished - 24 Jul 2020
EventInternational Joint Conference on Neural Networks: World Congress on Computational Intelligence - SEC, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020
Conference number: 48605X


ConferenceInternational Joint Conference on Neural Networks
Abbreviated titleIJCNN
CountryUnited Kingdom
Internet address


  • Capsule Neural Network
  • Modified Capsule Neural Network
  • capsules
  • pooling layer
  • scene recognition

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    Basu, A., Kaewrak, K., Petropoulakis, L., Di Caterina, G., & Soraghan, J. J. (2020). Modified Capsule Neural Network (Mod-CapsNet) for indoor home scene recognition. Paper presented at International Joint Conference on Neural Networks, Glasgow, United Kingdom.