Multi-scale pyramid pooling for deep convolutional representation

Compared to image representation based on low-level local descriptors, deep neural activations of Convolutional Neural Networks (CNNs) are richer in mid-level representation, but poorer in geometric invariance properties. In this paper, we present a straightforward framework for better image represe...

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Bibliographic Details
Main Authors: Yoo, Donggeun, Park, Sunggyun, Lee, Joon-Young, So Kweon, In
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Activation
Aggregates
Image recognition
Image representation
Indoor
Kernel
Neural networks
Object recognition
Proposals
Representations
State of the art
Support vector machines
Visualization
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Summary:Compared to image representation based on low-level local descriptors, deep neural activations of Convolutional Neural Networks (CNNs) are richer in mid-level representation, but poorer in geometric invariance properties. In this paper, we present a straightforward framework for better image representation by combining the two approaches. To take advantages of both representations, we extract a fair amount of multi-scale dense local activations from a pre-trained CNN. We then aggregate the activations by Fisher kernel framework, which has been modified with a simple scale-wise normalization essential to make it suitable for CNN activations. Our representation demonstrates new state-of-the-art performances on three public datasets: 80.78% (Acc.) on MIT Indoor 67, 83.20% (mAP) on PASCAL VOC 2007 and 91.28% (Acc.) on Oxford 102 Flowers. The results suggest that our proposal can be used as a primary image representation for better performances in wide visual recognition tasks.
ISSN:2160-7508
2160-7516
DOI:10.1109/CVPRW.2015.7301274