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Learning local feature aggregation functions with backpropagation


Conference Paper


This paper introduces a family of local feature aggregation functions and a novel method to estimate their parameters, such that they generate optimal representations for classification (or any task that can be expressed as a cost function minimization problem). To achieve that, we compose the local feature aggregation function with the classifier cost function and we backpropagate the gradient of this cost function in order to update the local feature aggregation function parameters. Experiments on synthetic datasets indicate that our method discovers parameters that model the class-relevant information in addition to the local feature space. Further experiments on a variety of motion and visual descriptors, both on image and video datasets, show that our method outperforms other state-of-the-art local feature aggregation functions, such as Bag of Words, Fisher Vectors and VLAD, by a large margin.

Author(s): Despoina Paschalidou and Angelos Katharopoulos and Christos Diou and Anastasios Delopoulos
Year: 2017
Month: August
Publisher: IEEE

Department(s): Autonomous Vision
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

DOI: 10.23919/EUSIPCO.2017.8081307
Event Name: Signal Processing Conference (EUSIPCO), 2017 25th European
Event Place: Kos Greece

Digital: True
State: Published
URL: http://ieeexplore.ieee.org/abstract/document/8081307/

Links: pdf
Attachments: poster


  title = {Learning local feature aggregation functions with backpropagation},
  author = {Paschalidou, Despoina and Katharopoulos, Angelos and Diou, Christos and Delopoulos, Anastasios},
  publisher = {IEEE},
  month = aug,
  year = {2017},
  url = {http://ieeexplore.ieee.org/abstract/document/8081307/},
  month_numeric = {8}