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231224s2016 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2015.2498407
|2 doi
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Jun Zhu
|e verfasserin
|4 aut
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| 245 |
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|a A Reconfigurable Tangram Model for Scene Representation and Categorization
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|c 2016
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|a Text
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|a ƒaComputermedien
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| 500 |
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|a Date Completed 18.03.2016
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|a Date Revised 11.03.2016
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a 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
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Tianfu Wu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Song-Chun Zhu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xiaokang Yang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wenjun Zhang
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 25(2016), 1 vom: 12. Jan., Seite 150-66
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|x 1941-0042
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|g volume:25
|g year:2016
|g number:1
|g day:12
|g month:01
|g pages:150-66
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2015.2498407
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