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Following the rapidly growing digital image usage, automatic imagecategorization has become preeminent research area. It has broaden and adoptedmany algorithms from time to time, whereby multi-feature (generally,hand-engineered features) based image characterization comes handy to improveaccuracy. Recently, in machine learning, pre-trained deep convolutional neuralnetworks (DCNNs or ConvNets) have been that the features extracted through suchDCNN can improve classification accuracy. Thence, in this paper, we furtherinvestigate a feature embedding strategy to exploit cues from multiple DCNNs.We derive a generalized feature space by embedding three different DCNNbottleneck features with weights respect to their Softmax cross-entropy loss.Test outcomes on six different object classification data-sets and an actionclassification data-set show that regardless of variation in image statisticsand tasks the proposed multi-DCNN bottleneck feature fusion is well suited toimage classification tasks and an effective complement of DCNN. The comparisonsto existing fusion-based image classification approaches prove that theproposed method surmounts the state-of-the-art methods and produces competitiveresults with fully trained DCNNs as well.

A Feature Embedding Strategy for High-level CNN representations from Multiple ConvNets