GEOMETRY-CONSTRAINED SPATIAL PYRAMID ADAPTATION FOR IMAGE CLASSIFICATION

Date

2014-10-30

Author

Tasli, H. Emrah
Sicre, Ronan
Gevers, Theo
Alatan, Abdullah Aydın

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This paper proposes a geometry-constrained spatial pyramid adaptation approach for the image classification task. Scene geometry is used as an input parameter for generating the spatial pyramid definitions. The resulting region adaptation is performed in accordance with the predefined geometric guidelines and underlying image characteristics. Using an approximate global geometric correspondence, exploits the idea that images of the same category share a spatial similarity. This assumption is evaluated and justified in an object classification framework, in which generated region segments are used as an enhancement to the widely utilized "spatial pyramid" method. Fixed region pyramids are replaced by the proposed locally coherent geometrically consistent region segments. Performance of the proposed method on object classification framework is evaluated on the 20 class Pascal VOC 2007 dataset. The proposed method shows consistent increase in the mean average precision (MAP) score for different experimental scenarios.

Subject Keywords

Image classification, Spatial pooling, Region segmentation

URI

https://hdl.handle.net/11511/55041

Conference Name

IEEE International Conference on Image Processing (ICIP)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

H. E. Tasli, R. Sicre, T. Gevers, and A. A. Alatan, “GEOMETRY-CONSTRAINED SPATIAL PYRAMID ADAPTATION FOR IMAGE CLASSIFICATION,” presented at the IEEE International Conference on Image Processing (ICIP), Paris, FRANCE, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55041.