  In this paper, we evaluate convolutional neural network (CNN) features using
the AlexNet architecture and very deep convolutional network (VGGNet)
architecture. To date, most CNN researchers have employed the last layers
before output, which were extracted from the fully connected feature layers.
However, since it is unlikely that feature representation effectiveness is
dependent on the problem, this study evaluates additional convolutional layers
that are adjacent to fully connected layers, in addition to executing simple
tuning for feature concatenation (e.g., layer 3 + layer 5 + layer 7) and
transformation, using tools such as principal component analysis. In our
experiments, we carried out detection and classification tasks using the
Caltech 101 and Daimler Pedestrian Benchmark Datasets.
