  We propose a novel video object segmentation algorithm based on pixel-level
matching using Convolutional Neural Networks (CNN). Our network aims to
distinguish the target area from the background on the basis of the pixel-level
similarity between two object units. The proposed network represents a target
object using features from different depth layers in order to take advantage of
both the spatial details and the category-level semantic information.
Furthermore, we propose a feature compression technique that drastically
reduces the memory requirements while maintaining the capability of feature
representation. Two-stage training (pre-training and fine-tuning) allows our
network to handle any target object regardless of its category (even if the
object's type does not belong to the pre-training data) or of variations in its
appearance through a video sequence. Experiments on large datasets demonstrate
the effectiveness of our model - against related methods - in terms of
accuracy, speed, and stability. Finally, we introduce the transferability of
our network to different domains, such as the infrared data domain.
