Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/634841
Title: A Reconfigurable Tangram Model for Scene Representation and Categorization
Authors: Jun Zhu;Tianfu Wu;Song-Chun Zhu;Xiaokang Yang;Wenjun Zhang
subject: And-Or Graph|Scene Layout|Tangram Model|Scene Categorization|Dynamic Programming
Year: 2016
Publisher: IEEE
Abstract: This paper presents a hierarchical and compositional scene layout (i.e., spatial configuration) representation and a method of learning reconfigurable model for scene categorization. Three types of shape primitives (i.e., triangle, parallelogram, and trapezoid), called tans, are used to tile scene image lattice in a hierarchical and compositional way, and a directed acyclic AND-OR graph (AOG) is proposed to organize the overcomplete dictionary of tan instances placed in image lattice, exploring a very large number of scene layouts. With certain off-the-shelf appearance features used for grounding terminal-nodes (i.e., tan instances) in the AOG, a scene layout is represented by the globally optimal parse tree learned via a dynamic programming algorithm from the AOG, which we call tangram model. Then, a scene category is represented by a mixture of tangram models discovered with an exemplar-based clustering method. On basis of the tangram model, we address scene categorization in two aspects: 1) building a tangram bank representation for linear classifiers, which utilizes a collection of tangram models learned from all categories and 2) building a tangram matching kernel for kernel-based classification, which accounts for all hidden spatial configurations in the AOG. In experiments, our methods are evaluated on three scene data sets for both the configuration-level and semantic-level scene categorization, and outperform the spatial pyramid model consistently.
URI: http://localhost/handle/Hannan/167480
http://localhost/handle/Hannan/634841
ISSN: 1057-7149
1941-0042
volume: 25
issue: 1
Appears in Collections:2016

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Title: A Reconfigurable Tangram Model for Scene Representation and Categorization
Authors: Jun Zhu;Tianfu Wu;Song-Chun Zhu;Xiaokang Yang;Wenjun Zhang
subject: And-Or Graph|Scene Layout|Tangram Model|Scene Categorization|Dynamic Programming
Year: 2016
Publisher: IEEE
Abstract: This paper presents a hierarchical and compositional scene layout (i.e., spatial configuration) representation and a method of learning reconfigurable model for scene categorization. Three types of shape primitives (i.e., triangle, parallelogram, and trapezoid), called tans, are used to tile scene image lattice in a hierarchical and compositional way, and a directed acyclic AND-OR graph (AOG) is proposed to organize the overcomplete dictionary of tan instances placed in image lattice, exploring a very large number of scene layouts. With certain off-the-shelf appearance features used for grounding terminal-nodes (i.e., tan instances) in the AOG, a scene layout is represented by the globally optimal parse tree learned via a dynamic programming algorithm from the AOG, which we call tangram model. Then, a scene category is represented by a mixture of tangram models discovered with an exemplar-based clustering method. On basis of the tangram model, we address scene categorization in two aspects: 1) building a tangram bank representation for linear classifiers, which utilizes a collection of tangram models learned from all categories and 2) building a tangram matching kernel for kernel-based classification, which accounts for all hidden spatial configurations in the AOG. In experiments, our methods are evaluated on three scene data sets for both the configuration-level and semantic-level scene categorization, and outperform the spatial pyramid model consistently.
URI: http://localhost/handle/Hannan/167480
http://localhost/handle/Hannan/634841
ISSN: 1057-7149
1941-0042
volume: 25
issue: 1
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7321021.pdf5.41 MBAdobe PDFThumbnail
Preview File
Title: A Reconfigurable Tangram Model for Scene Representation and Categorization
Authors: Jun Zhu;Tianfu Wu;Song-Chun Zhu;Xiaokang Yang;Wenjun Zhang
subject: And-Or Graph|Scene Layout|Tangram Model|Scene Categorization|Dynamic Programming
Year: 2016
Publisher: IEEE
Abstract: This paper presents a hierarchical and compositional scene layout (i.e., spatial configuration) representation and a method of learning reconfigurable model for scene categorization. Three types of shape primitives (i.e., triangle, parallelogram, and trapezoid), called tans, are used to tile scene image lattice in a hierarchical and compositional way, and a directed acyclic AND-OR graph (AOG) is proposed to organize the overcomplete dictionary of tan instances placed in image lattice, exploring a very large number of scene layouts. With certain off-the-shelf appearance features used for grounding terminal-nodes (i.e., tan instances) in the AOG, a scene layout is represented by the globally optimal parse tree learned via a dynamic programming algorithm from the AOG, which we call tangram model. Then, a scene category is represented by a mixture of tangram models discovered with an exemplar-based clustering method. On basis of the tangram model, we address scene categorization in two aspects: 1) building a tangram bank representation for linear classifiers, which utilizes a collection of tangram models learned from all categories and 2) building a tangram matching kernel for kernel-based classification, which accounts for all hidden spatial configurations in the AOG. In experiments, our methods are evaluated on three scene data sets for both the configuration-level and semantic-level scene categorization, and outperform the spatial pyramid model consistently.
URI: http://localhost/handle/Hannan/167480
http://localhost/handle/Hannan/634841
ISSN: 1057-7149
1941-0042
volume: 25
issue: 1
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7321021.pdf5.41 MBAdobe PDFThumbnail
Preview File