  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.
