Weakly-Supervised Learning of a Deep Convolutional Neural Networks for Semantic Segmentation - Yanqing Feng; Lunwen Wang; Mengbo Zhang (IEEE Access, 2019)
Deep convolutional neural networks (DCNNs) trained on the pixel-wise annotated images have dramatically improved the state-of-the-art in semantic segmentation. However, due to the high cost of labeling training data, its application has great limitation. In this paper, we propose a DCNNs model for generating the pixel-level labels using the image-level annotation. The model consists of an encoder-decoder, a feature decomposer, and a multi-label classifier. The encoder extracts the deep convolutional feature maps of the input image. The feature decomposer can decompose the convolutional feature extracted by the encoder into feature components of different semantics. The decomposer is based on the orthogonal non-negative matrix factorization (NMF) technology. The function of the decoder is to map the feature components of different semantics to the input resolution images. The decoder uses the position index of maximum pooling provided by the corresponding encoder to perform non-linear up-sampling, which eliminates the learning requirement of up-sampling. Since the image reconstruction is conducted according to the semantic categories, image regions of different semantics are restored to different images. Then, the regions of different semantics can be segmented through the posted-processing algorithms. The experimental results on open data set show that the proposed model outperforms some recently developed methods.