  Despite the effectiveness of convolutional neural networks (CNNs) especially
in image classification tasks, the effect of convolution features on learned
representations is still limited. It mostly focuses on the salient object of
the images, but ignores the variation information on clutter and local. In this
paper, we propose a special framework, which is the multiple VLAD encoding
method with the CNNs features for image classification. Furthermore, in order
to improve the performance of the VLAD coding method, we explore the
multiplicity of VLAD encoding with the extension of three kinds of encoding
algorithms, which are the VLAD-SA method, the VLAD-LSA and the VLAD-LLC method.
Finally, we equip the spatial pyramid patch (SPM) on VLAD encoding to add the
spatial information of CNNs feature. In particular, the power of SPM leads our
framework to yield better performance compared to the existing method.
