Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82238
Title: Discovering Class-Specific Spatial Layouts for Scene Recognition
Authors: Weng, Chaoqun
Wang, Hongxing
Yuan, Junsong
Jiang, Xudong
Keywords: Discovering class-specific spatial layouts
Scene recognition
Issue Date: 2016
Source: Weng, C., Wang, H., Yuan, J., & Jiang, X. (2017). Discovering Class-Specific Spatial Layouts for Scene Recognition. IEEE Signal Processing Letters, 24(8), 1143-1147.
Series/Report no.: IEEE Signal Processing Letters
Abstract: Scene image is a spatial composition of objects and background contexts and finding discriminative spatial layouts is critical for scene recognition. In this letter, we propose an ℓ1-regularized max-margin formulation to discover class-specific spatial layouts by jointly learning the image classifier and the class-specific spatial layouts for scene recognition. Unlike previous methods that classify images into different categories either without considering the spatial layouts explicitly or only using class generic spatial layout, our proposed method can discover a sparse combination of class-specific spatial layouts for different scenes and boost the recognition performance. Experiments on scene-15, landuse-21, and MIT indoor-67 datasets validate the advantages of our proposed algorithm.
URI: https://hdl.handle.net/10356/82238
http://hdl.handle.net/10220/43502
ISSN: 1070-9908
DOI: 10.1109/LSP.2016.2641020
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/LSP.2016.2641020].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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