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- Publisher Website: 10.1007/978-3-642-11301-7_24
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Conference Paper: Discovering class-specific informative patches and its application in landmark charaterization
Title | Discovering class-specific informative patches and its application in landmark charaterization |
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Authors | |
Keywords | BoW Image Informative Patch Multi-Ranking Amalgamation Strategy |
Issue Date | 2009 |
Citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, v. 5916 LNCS, p. 218-228 How to Cite? |
Abstract | Discovering class-specific informative regions for a given concept with a few images is an interesting but very challenging task, due to occlusion, scale changes of objects, as well as different views under varying lighting conditions. This paper proposes a new perspective to discover the informative regions by using several images. To achieve this, we introduce a new representation of image: Ordered-BoW Image (BoWI), whose elements summarizes information of the patch centered at the element in original image. Because of its "structured pixels", BoWI is robust and informative enough for an object class representation. Histogram-based Multi-Ranking Amalgamation Strategy (MRAS) is adopted to explore the most informative patches for an object in BoWI. Experiments on Landmark-National Icon data set that our approach is robust to occlusion, scale and illumination, and achieves promising performance in discovering class-specific informative regions. © 2010 Springer-Verlag Berlin Heidelberg. |
Persistent Identifier | http://hdl.handle.net/10722/345052 |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
DC Field | Value | Language |
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dc.contributor.author | Gao, Shenghua | - |
dc.contributor.author | Cheng, Xiangang | - |
dc.contributor.author | Chia, Liang Tien | - |
dc.date.accessioned | 2024-08-15T09:24:54Z | - |
dc.date.available | 2024-08-15T09:24:54Z | - |
dc.date.issued | 2009 | - |
dc.identifier.citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, v. 5916 LNCS, p. 218-228 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/345052 | - |
dc.description.abstract | Discovering class-specific informative regions for a given concept with a few images is an interesting but very challenging task, due to occlusion, scale changes of objects, as well as different views under varying lighting conditions. This paper proposes a new perspective to discover the informative regions by using several images. To achieve this, we introduce a new representation of image: Ordered-BoW Image (BoWI), whose elements summarizes information of the patch centered at the element in original image. Because of its "structured pixels", BoWI is robust and informative enough for an object class representation. Histogram-based Multi-Ranking Amalgamation Strategy (MRAS) is adopted to explore the most informative patches for an object in BoWI. Experiments on Landmark-National Icon data set that our approach is robust to occlusion, scale and illumination, and achieves promising performance in discovering class-specific informative regions. © 2010 Springer-Verlag Berlin Heidelberg. | - |
dc.language | eng | - |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.subject | BoW Image | - |
dc.subject | Informative Patch | - |
dc.subject | Multi-Ranking Amalgamation Strategy | - |
dc.title | Discovering class-specific informative patches and its application in landmark charaterization | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-642-11301-7_24 | - |
dc.identifier.scopus | eid_2-s2.0-77249171234 | - |
dc.identifier.volume | 5916 LNCS | - |
dc.identifier.spage | 218 | - |
dc.identifier.epage | 228 | - |
dc.identifier.eissn | 1611-3349 | - |