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Robust Scene Classification with Cross-Level LLC Coding on CNN Features

  • Zequn JieEmail author
  • Shuicheng Yan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)

Abstract

Convolutional Neural Network (CNN) features have demonstrated outstanding performance as global representations for image classification, but they lack invariance to scale transformation, which makes it difficult to adapt to various complex tasks such as scene classification. To strengthen the scale invariance of CNN features and meanwhile retain their powerful discrimination in scene classification, we propose a framework where cross-level Locality-constrained Linear Coding and cascaded fine-tuned CNN features are combined, which is shorted as cross-level LLC-CNN. Specifically, this framework first fine-tunes multi-level CNNs in a cascaded way, then extracts multi-level CNN features to learn a cross-level universal codebook, and finally performs locality-constrained linear coding (LLC) and max-pooling on the patches of all levels to form the final representation. It is experimentally verified that the LLC responses on the universal codebook outperform the CNN features and achieve the state-of-the-art performance on the two currently largest scene classification benchmarks, MIT Indoor Scenes and SUN 397.

Keywords

Recognition Accuracy Convolutional Neural Network Code Word Scale Transformation Scene Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This research is supported by the National Research Foundation, Prime Ministers Office, Singapore under its International Research Centre @ Singapore Funding Initiative and administered by the Interactive&Digital Media Programme Office.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Keio-NUS CUTE CenterNational University of SingaporeSingaporeSingapore
  2. 2.Department of Electrical and Computer EngineeringNational University of SingaporeSingaporeSingapore

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