  New efficient source feature compression solutions are proposed based on a
two-stage Walsh-Hadamard Transform (WHT) for Convolutional Neural Network
(CNN)-based object classification in underwater robotics. The object images are
firstly transformed by WHT following a two-stage process. The transform-domain
tensors have large values concentrated in the upper left corner of the matrices
in the RGB channels. By observing this property, the transform-domain matrix is
partitioned into inner and outer regions. Consequently, two novel partitioning
methods are proposed in this work: (i) fixing the size of inner and outer
regions; and (ii) adjusting the size of inner and outer regions adaptively per
image. The proposals are evaluated with an underwater object dataset captured
from the Raritan River in New Jersey, USA. It is demonstrated and verified that
the proposals reduce the training time effectively for learning-based
underwater object classification task and increase the accuracy compared with
the competing methods. The object classification is an essential part of a
vision-based underwater robot that can sense the environment and navigate
autonomously. Therefore, the proposed method is well-suited for efficient
computer vision-based tasks in underwater robotics applications.
