Leveraging Implicit Spatial Information In Global Features For Image Retrieval
Résumé
Most image retrieval methods use global features that aggregate local
distinctive patterns into a single representation. However, the
aggregation process destroys the relative spatial information by considering
orderless sets of local descriptors. We propose to integrate
relative spatial information into the aggregation process by taking
into account co-occurrences of local patterns in a tensor framework.
The resulting signature called Improved Spatial Tensor Aggregation
(ISTA) is able to reach state of the art performances on well known
datasets such as Holidays, Oxford5k and Paris6k.