Encoding Spatial Distribution of Convolutional Features for Texture RepresentationDownload PDF

21 May 2021, 20:44 (modified: 26 Oct 2021, 17:52)NeurIPS 2021 PosterReaders: Everyone
Keywords: Deep Learning, Feature Encoding, Global Pooling, Texture Representation
TL;DR: Proposing a feature encoding module for CNN-based texture representation which is driven by hierarchical fractal geometry analysis
Abstract: Existing convolutional neural networks (CNNs) often use global average pooling (GAP) to aggregate feature maps into a single representation. However, GAP cannot well characterize complex distributive patterns of spatial features while such patterns play an important role in texture-oriented applications, e.g., material recognition and ground terrain classification. In the context of texture representation, this paper addressed the issue by proposing Fractal Encoding (FE), a feature encoding module grounded by multi-fractal geometry. Considering a CNN feature map as a union of level sets of points lying in the 2D space, FE characterizes their spatial layout via a local-global hierarchical fractal analysis which examines the multi-scale power behavior on each level set. This enables a CNN to encode the regularity on the spatial arrangement of image features, leading to a robust yet discriminative spectrum descriptor. In addition, FE has trainable parameters for data adaptivity and can be easily incorporated into existing CNNs for end-to-end training. We applied FE to ResNet-based texture classification and retrieval, and demonstrated its effectiveness on several benchmark datasets.
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Code: https://github.com/csfengli/FENet
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