Several recent works interpret convolutional features produced

by deep convolutional neural networks as local descriptors.

Existing high-dimensional aggregation based methods,

e.g., Fisher Vector (FV) obtain inferior performance to pooling

based methods in most situations, and we observe that

it is mainly caused by the ignorance of spatial weights. In

this paper, we propose a novel method named spatial weighted

Fisher Vector (SWFV) to enhance the representation of

FV by injecting the spatial weight map to FV. In addition,

we further analyze the distribution of spatial weights and propose

truncated spatial weighted FV (TSWFV). Experimental

results on two benchmark datasets demonstrate that the two

proposed methods achieve competitive results compared with

other global representation based methods.