Single image vehicle classification using pseudo long short-term memory classifier

Reza Fuad Rachmadi*, Keiichi Uchimura, Gou Koutaki, Kohichi Ogata

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


In this paper, we propose a pseudo long short-term memory (LSTM) classifier for single image vehicle classification. The proposed pseudo-LSTM (P-LSTM) uses spatially divided images rather than time-series images. In other words, the proposed method considers the divided images to be time-series frames. The divided images are formed by cropping input images using two-level spatial pyramid region configuration. Parallel convolutional networks are used to extract the spatial pyramid features of the divided images. To explore the correlations between the spatial pyramid features, we attached an LSTM classifier to the end of the parallel convolutional network and treated each convolutional network as an independent timestamp. Although LSTM classifiers are typically used for time-dependent data, our experiments demonstrated that they can also be used for non-time-dependent data. We attached one fully connected layer to the end of the network to compute a final classification decision. Experiments on an MIO-TCD vehicle classification dataset show that our proposed classifier produces a high evaluation score and is comparable with several other state-of-the-art methods.

Original languageEnglish
Pages (from-to)265-274
Number of pages10
JournalJournal of Visual Communication and Image Representation
Publication statusPublished - Oct 2018


  • Deep convolutional network
  • Pseudo-LSTM classifier
  • Vehicle classification


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