Indoor Scene Recognition: An Attention-Based Approach Using Feature Selection-Based Transfer Learning and Deep Liquid State Machine

Ranjini Surendran, Ines Chihi, J. Anitha, D. Jude Hemanth

Scene understanding is one of the most challenging areas of research in the fields of robotics and computer vision. Recognising indoor scenes is one of the research applications in the category of scene understanding that has gained attention in recent years. Recent developments in deep learning and transfer learning approaches have attracted huge attention in addressing this challenging area. In our work, we have proposed a fine-tuned deep transfer learning approach using DenseNet201 for feature extraction and a deep Liquid State Machine model as the classifier in order to develop a model for recognising and understanding indoor scenes. We have included fuzzy colour stacking techniques, colour-based segmentation, and an adaptive World Cup optimisation algorithm to improve the performance of our deep model. Our proposed model would dedicatedly assist the visually impaired and blind to navigate in the indoor environment and completely integrate into their day-to-day activities. Our proposed work was implemented on the NYU depth dataset and attained an accuracy of 96% for classifying the indoor scenes.