The devil is in the details: an evaluation of recent feature encoding methods

Conference item

A large number of novel encodings for bag of visual words models have been proposed in the past two years to improve on the standard histogram of quantized local features. Examples include locality-constrained linear encoding [23], improved Fisher encoding [17], super vector encoding [27], and kernel codebook encoding [20]. While several authors have reported very good results on the challenging PASCAL VOC classification data by means of these new techniques, differences in the feature computation and learning algorithms, missing details in the description of the methods, and different tuning of the various components, make it impossible to compare directly these methods and hard to reproduce the results reported. This paper addresses these shortcomings by carrying out a rigorous evaluation of these new techniques by: (1) fixing the other elements of the pipeline (features, learning, tuning); (2) disclosing all the implementation details, and (3) identifying both those aspects of each method which are particularly important to achieve good performance, and those aspects which are less critical. This allows a consistent comparative analysis of these encoding methods. Several conclusions drawn from our analysis cannot be inferred from the original publications.

Authors: Chatfield, K; Lempitsky, V; Vedaldi, A; Zisserman, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: British Machine Vision Association
• Host title: Proceedings of the British Machine Vision Conference 2011
• Pages: 76.1-76.12
• Publication date: 2011-10-11
• Acceptance date: 2011-06-28
• Event title: 22nd British Machine Vision Conference (BMVC 2011)
• Event location: Dundee, Scotland
• Event website: https://bmva-archive.org.uk/bmvc/2011/index.html
• Event start date: 2011-08-29
• Event end date: 2011-09-02
• ISBN: 190172543X
• Language: English