Visual Instance Retrieval with Deep Convolutional Networks
This work addresses image retrieval for computer vision applications, presenting an incremental improvement over existing methods.
The paper tackled visual instance retrieval by studying convolutional network image representations and an efficient multi-scale pipeline with geometric invariance, achieving state-of-the-art performance on five standard datasets.
This paper provides an extensive study on the availability of image representations based on convolutional networks (ConvNets) for the task of visual instance retrieval. Besides the choice of convolutional layers, we present an efficient pipeline exploiting multi-scale schemes to extract local features, in particular, by taking geometric invariance into explicit account, i.e. positions, scales and spatial consistency. In our experiments using five standard image retrieval datasets, we demonstrate that generic ConvNet image representations can outperform other state-of-the-art methods if they are extracted appropriately.