Exemplar-Based Image and Video Stylization Using Fully Convolutional Semantic Features

Image stylization with a painting machine using semantic hints

In this paper we present and evaluate painterly rendering techniques that work within a visual feedback loop of eDavid, our painting robot. The machine aims at simulating the human painting process. Two such methods and their semantics driven combination are compared for different objects. One uses a predefined set of stroke candidates, the other creates strokes directly using line integral con...

Learning Document Image Features With SqueezeNet Convolutional Neural Network

The classification of various document images is considered an important step towards building a modern digital library or office automation system. Convolutional Neural Network (CNN) classifiers trained with backpropagation are considered to be the current state of the art model for this task. However, there are two major drawbacks for these classifiers: the huge computational power demand for...

Beyond visual features: A weak semantic image representation using exemplar classifiers for classification

Usually, the low-level representation of images is unsatisfied for image classification due to the well-known semantic gap, and further hinders its application for high-level visual applications. To deal with these problems, in this paper, we propose a simple but effective image representation for image classification, which is denoted as the responses to a set of exemplar image classifiers. Ea...

High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks

We propose a method for high-performance semantic image segmentation (or semantic pixel labelling) based on very deep residual networks, which achieves the state-of-the-art performance. A few design factors are carefully considered to this end. We make the following contributions. (i) First, we evaluate different variations of a fully convolutional residual network so as to find the best config...

Semantic-Based Image Retrial in the VQ Compressed Domain using Image Annotation Statistical Models