Efficient Convolutional Neural Network with Binary Quantization Layer
This addresses the challenge of efficient and general semantic image segmentation for applications requiring real-time processing, though it appears incremental as it builds on existing CNN frameworks.
The paper tackles the problem of high-dimensional CNN feature clustering for image segmentation by introducing binary quantization layers, achieving real-time performance and outperforming state-of-the-art non-semantic segmentation methods by a large margin.
In this paper we introduce a novel method for segmentation that can benefit from general semantics of Convolutional Neural Network (CNN). Our segmentation proposes visually and semantically coherent image segments. We use binary encoding of CNN features to overcome the difficulty of the clustering on the high-dimensional CNN feature space. These binary encoding can be embedded into the CNN as an extra layer at the end of the network. This results in real-time segmentation. To the best of our knowledge our method is the first attempt on general semantic image segmentation using CNN. All the previous papers were limited to few number of category of the images (e.g. PASCAL VOC). Experiments show that our segmentation algorithm outperform the state-of-the-art non-semantic segmentation methods by a large margin.