Multi-scale Orderless Pooling of Deep Convolutional Activation Features View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2014

AUTHORS

Yunchao Gong , Liwei Wang , Ruiqi Guo , Svetlana Lazebnik

ABSTRACT

Deep convolutional neural networks (CNN) have shown their promise as a universal representation for recognition. However, global CNN activations lack geometric invariance, which limits their robustness for classification and matching of highly variable scenes. To improve the invariance of CNN activations without degrading their discriminative power, this paper presents a simple but effective scheme called multi-scale orderless pooling (MOP-CNN). This scheme extracts CNN activations for local patches at multiple scale levels, performs orderless VLAD pooling of these activations at each level separately, and concatenates the result. The resulting MOP-CNN representation can be used as a generic feature for either supervised or unsupervised recognition tasks, from image classification to instance-level retrieval; it consistently outperforms global CNN activations without requiring any joint training of prediction layers for a particular target dataset. In absolute terms, it achieves state-of-the-art results on the challenging SUN397 and MIT Indoor Scenes classification datasets, and competitive results on ILSVRC2012/2013 classification and INRIA Holidays retrieval datasets. More... »

PAGES

392-407

References to SciGraph publications

Book

TITLE

Computer Vision – ECCV 2014

ISBN

978-3-319-10583-3
978-3-319-10584-0

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-10584-0_26

DOI

http://dx.doi.org/10.1007/978-3-319-10584-0_26

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1032984348


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