  Recognizing apparel attributes has recently drawn great interest in the
computer vision community. Methods based on various deep neural networks have
been proposed for image classification, which could be applied to apparel
attributes recognition. An interesting problem raised is how to ensemble these
methods to further improve the accuracy. In this paper, we propose a two-layer
mixture framework for ensemble different networks. In the first layer of this
framework, two types of ensemble learning methods, bagging and boosting, are
separately applied. Different from traditional methods, our bagging process
makes use of the whole training set, not random subsets, to train each model in
the ensemble, where several differentiated deep networks are used to promote
model variance. To avoid the bias of small-scale samples, the second layer only
adopts bagging to mix the results obtained with bagging and boosting in the
first layer. Experimental results demonstrate that the proposed mixture
framework outperforms any individual network model or either independent
ensemble method in apparel attributes classification.
