A Feature Embedding Strategy for High-level CNN representations from Multiple ConvNets
  Following the rapidly growing digital image usage, automatic image
categorization has become preeminent research area. It has broaden and adopted
many algorithms from time to time, whereby multi-feature (generally,
hand-engineered features) based image characterization comes handy to improve
accuracy. Recently, in machine learning, pre-trained deep convolutional neural
networks (DCNNs or ConvNets) have been that the features extracted through such
DCNN can improve classification accuracy. Thence, in this paper, we further
investigate a feature embedding strategy to exploit cues from multiple DCNNs.
We derive a generalized feature space by embedding three different DCNN
bottleneck features with weights respect to their Softmax cross-entropy loss.
Test outcomes on six different object classification data-sets and an action
classification data-set show that regardless of variation in image statistics
and tasks the proposed multi-DCNN bottleneck feature fusion is well suited to
image classification tasks and an effective complement of DCNN. The comparisons
to existing fusion-based image classification approaches prove that the
proposed method surmounts the state-of-the-art methods and produces competitive
results with fully trained DCNNs as well.
