title: What Is the Best Practice for CNNs Applied to Visual Instance Retrieval?
abstract: Previous work has shown that feature maps of deep convolutional neural networks (CNNs)
  can be interpreted as feature representation of a particular image region. Features aggregated from
  these feature maps have been exploited for image retrieval tasks and achieved state-of-the-art performances in
  recent years. The key to the success of such methods is the feature representation. However, the different
  factors that impact the effectiveness of features are still not explored thoroughly. There are much less
  discussion about the best combination of them.
  
  The main contribution of our paper is the thorough evaluations of the various factors that affect the
  discriminative ability of the features extracted from CNNs. Based on the evaluation results, we also identify 
  the best choices for different factors and propose a new multi-scale image feature representation method to 
  encode the image effectively. Finally, we show that the proposed method generalises well and outperforms 
  the state-of-the-art methods on four typical datasets used for visual instance retrieval.
authors: Jiedong Hao Jing Dong Wei Wang Tieniu Tan
hIndex: Tieniu Tan 98