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Complex background classification network: A deep learning method for urban images classification
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DOI：10.1016/j.compeleceng.2020.106771.png)
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Abstract
En 中文
Urban images usually contain buildings, pedestrians, vehicles, roads, and other complex objects. Due to this high complexity, object detection and classification of urban images is a challenging task. In this paper, a novel convolutional neural network named complex background classification network (CBC-Net) is proposed for urban image classification. Unlike the existing image classification and object detection methods such as residual network (ResNet) and faster region-based convolutional neural networks (Faster R-CNN), CBC-Net first utilizes a multilayer perceptron convolutional layer instead of a linear convolutional layer to extract representative features from urban images, and then uses a back-propagation neural network to optimize the extracted object parameters. In addition, we build a standard urban image dataset (UID) which contains eight categories. Qualitative experiments on two benchmark datasets demonstrate that classification accuracy and computation of CBCNet outperform the state-of-the-art methods. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Urban image
Classification
Urban computing
Object detection
Deep learning
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