Novel Scene Recognition Using TrainDetector

  • Sebastien MambouEmail author
  • Ondrej Krejcar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)


Our ability to process the image keeps improving day by day, since the introduction of deep learning. Lastly, this contributed to the advance of object recognition through a Convolutional neural network and Place recognition, which is our concern in this paper. Through this research, it was observed a complexity in the extraction of the correct and relevant features for scene recognition. To address this issue, we extracted at the pixel level several subareas which contain more color intensity than other parts, and we went through each image once to build the feature representation of it. We also noticed that several available models based on Convolution Neural Network requires a Graphics Processing Units (GPU) for their implementation and are difficult to train. We propose in this paper, a novel Scene Recognition method using Single-Shot-Detector (SSD), Multi-modal Local-Receptive-Field (MM-LRF) and Extreme-Learning-Machine (ELM) that we named TrainDetector. It outperforms the state-of-the-art techniques when we apply it to three well-known scene recognition Datasets.


SSD MM-LRF ELM TrainDetector 



The work and the contribution were supported by the SPEV project “Smart Solutions in Ubiquitous Computing Environments 2018”, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Center for Basic and Applied Research, Faculty of Informatics and ManagementUniversity of Hradec KraloveHradec KraloveCzech Republic

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