{"title":"Object localization with global and local context kernels","quality_controlled":0,"acknowledgement":"The research leading to these results has received funding from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007- 2013) / ERC grant agreement no. 228180. This work was funded in part by the EC project CLASS, IST 027978, and the PASCAL2 network of excellence. The first author is supported by the Royal Academy of Engineering through a Newton International Fellowship.","status":"public","alternative_title":["Proceedings of the BMVC"],"date_created":"2018-12-11T12:04:42Z","publist_id":"2655","publisher":"BMVA Press","year":"2009","extern":1,"conference":{"name":"BMVC: British Machine Vision Conference"},"citation":{"short":"M. Blaschko, C. Lampert, in:, BMVA Press, 2009, pp. 1–11.","chicago":"Blaschko, Matthew, and Christoph Lampert. “Object Localization with Global and Local Context Kernels,” 1–11. BMVA Press, 2009. https://doi.org/10.5244/C.23.63.","ista":"Blaschko M, Lampert C. 2009. Object localization with global and local context kernels. BMVC: British Machine Vision Conference, Proceedings of the BMVC, , 1–11.","apa":"Blaschko, M., & Lampert, C. (2009). Object localization with global and local context kernels (pp. 1–11). Presented at the BMVC: British Machine Vision Conference, BMVA Press. https://doi.org/10.5244/C.23.63","ama":"Blaschko M, Lampert C. Object localization with global and local context kernels. In: BMVA Press; 2009:1-11. doi:10.5244/C.23.63","ieee":"M. Blaschko and C. Lampert, “Object localization with global and local context kernels,” presented at the BMVC: British Machine Vision Conference, 2009, pp. 1–11.","mla":"Blaschko, Matthew, and Christoph Lampert. Object Localization with Global and Local Context Kernels. BMVA Press, 2009, pp. 1–11, doi:10.5244/C.23.63."},"date_published":"2009-09-10T00:00:00Z","publication_status":"published","_id":"3703","abstract":[{"lang":"eng","text":"Recent research has shown that the use of contextual cues significantly improves performance in sliding window type localization systems. In this work, we propose a method that incorporates both global and local context information through appropriately defined kernel functions. In particular, we make use of a weighted combination of kernels defined over local spatial regions, as well as a global context kernel. The relative importance of the context contributions is learned automatically, and the resulting discriminant function is of a form such that localization at test time can be solved efficiently using a branch and bound optimization scheme. By specifying context directly with a kernel learning approach, we achieve high localization accuracy with a simple and efficient representation. This is in contrast to other systems that incorporate context for which expensive inference needs to be done at test time. We show experimentally on the PASCAL VOC datasets that the inclusion of context can significantly improve localization performance, provided the relative contributions of context cues are learned appropriately."}],"main_file_link":[{"open_access":"0","url":"http://www.bmva.org/bmvc/2009/Papers/Paper228/Paper228.pdf"}],"type":"conference","doi":"10.5244/C.23.63","page":"1 - 11","author":[{"last_name":"Blaschko","first_name":"Matthew","full_name":"Blaschko,Matthew B"},{"last_name":"Lampert","first_name":"Christoph","full_name":"Christoph Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887"}],"day":"10","month":"09","tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_updated":"2021-01-12T07:51:36Z"}