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Bounding Boxes Are All We Need: Street View Image Classification via Context Encoding of Detected Buildings | | Kun Zhao
; Yongkun Liu
; Siyuan Hao
; Shaoxing Lu
; Hongbin Liu
; Lijian Zhou
; | | Date: |
3 Oct 2020 | | Abstract: | Street view images have been increasingly used in tasks like urban land use
classification and urban functional zone portraying. Street view image
classification is difficult because the class labels such as commercial area,
are concepts with higher abstract level compared to general visual tasks.
Therefore, classification models using only visual features often fail to
achieve satisfactory performance. We believe that the efficient representation
of significant objects and their context relations in street view images are
the keys to solve this problem. In this paper, a novel approach based on a
detector-encoder-classifier framework is proposed. Different from common
image-level end-to-end models, our approach does not use visual features of the
whole image directly. The proposed framework obtains the bounding boxes of
buildings in street view images from a detector. Their contextual information
such as building classes and positions are then encoded into metadata and
finally classified by a recurrent neural network (RNN). To verify our approach,
we made a dataset of 19,070 street view images and 38,857 buildings based on
the BIC_GSV dataset through a combination of automatic label acquisition and
expert annotation. The dataset can be used not only for street view image
classification aiming at urban land use analysis, but also for multi-class
building detection. Experiments show that the proposed approach achieves a
12.65% performance improvement on macro-precision and 12% on macro-recall over
the models based on end-to-end convolutional neural network (CNN). Our code and
dataset are available at
this https URL | | Source: | arXiv, 2010.01305 | | Services: | Forum | Review | PDF | Favorites | |
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