  Examining locomotion has improved our basic understanding of motor control
and aided in treating motor impairment. Mice and rats are the model system of
choice for basic neuroscience studies of human disease. High frame rates are
needed to quantify the kinematics of running rodents, due to their high stride
frequency. Manual tracking, especially for multiple body landmarks, becomes
extremely time-consuming. To overcome these limitations, we proposed the use of
superpixels based image segmentation as superpixels utilized both spatial and
color information for segmentation. We segmented some parts of body and tested
the success of segmentation as a function of color space and SLIC segment size.
We used a simple merging function to connect the segmented regions considered
as neighbor and having the same intensity value range. In addition, 28 features
were extracted, and t-SNE was used to demonstrate how much the methods are
capable to differentiate the regions. Finally, we compared the segmented
regions to a manually outlined region. The results showed for segmentation,
using the RGB image was slightly better compared to the hue channel. For merg-
ing and classification, however, the hue representation was better as it
captures the relevant color information in a single channel.
