{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/encoding-spatial-distribution-of","title":"Encoding Spatial Distribution of Convolutional Features for Texture Representation","arxiv_id":null,"date":"2021-12-01","proceeding":"NeurIPS 2021 12","authors":["Yong Xu","Feng Li","Zhile Chen","Jinxiu Liang","Yuhui Quan"],"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.","url_abs":"http://proceedings.neurips.cc/paper/2021/hash/c04c19c2c2474dbf5f7ac4372c5b9af1-Abstract.html","url_pdf":"http://proceedings.neurips.cc/paper/2021/file/c04c19c2c2474dbf5f7ac4372c5b9af1-Paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"encoding-spatial-distribution-of","repo_url":"https://github.com/csfengli/fenet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"material-recognition","task_name":"Material Recognition"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"texture-classification","task_name":"Texture Classification"}],"methods":[{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}