  With the impressive capability to capture visual content, deep convolutional
neural networks (CNN) have demon- strated promising performance in various
vision-based ap- plications, such as classification, recognition, and objec- t
detection. However, due to the intrinsic structure design of CNN, for images
with complex content, it achieves lim- ited capability on invariance to
translation, rotation, and re-sizing changes, which is strongly emphasized in
the s- cenario of content-based image retrieval. In this paper, to address this
problem, we proposed a new kernelized deep convolutional neural network. We
first discuss our motiva- tion by an experimental study to demonstrate the
sensitivi- ty of the global CNN feature to the basic geometric trans-
formations. Then, we propose to represent visual content with approximate
invariance to the above geometric trans- formations from a kernelized
perspective. We extract CNN features on the detected object-like patches and
aggregate these patch-level CNN features to form a vectorial repre- sentation
with the Fisher vector model. The effectiveness of our proposed algorithm is
demonstrated on image search application with three benchmark datasets.
