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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Nianhua Xie Haibin Ling Weiming Hu Xiaoqin Zhang |
| Copyright Year | 2010 |
| Description | Author affiliation: Dept. of Computer and Information Science, Temple University, Philadelphia, USA (Haibin Ling) || National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China (Nianhua Xie; Weiming Hu; Xiaoqin Zhang) |
| Abstract | In this paper we propose using bin-ratio information, which is collected from the ratios between bin values of histograms, for scene and category classification. To use such information, a new histogram dissimilarity, bin-ratio dissimilarity (BRD), is designed. We show that BRD provides several attractive advantages for category and scene classification tasks: First, BRD is robust to cluttering, partial occlusion and histogram normalization; Second, BRD captures rich co-occurrence information while enjoying a linear computational complexity; Third, BRD can be easily combined with other dissimilarity measures, such as L1 and $χ^{2},$ to gather complimentary information. We apply the proposed methods to category and scene classification tasks in the bag-of-words framework. The experiments are conducted on several widely tested datasets including PASCAL 2005, PASCAL 2008, Oxford flowers, and Scene-15 dataset. In all experiments, the proposed methods demonstrate excellent performance in comparison with previously reported solutions. |
| Starting Page | 2313 |
| Ending Page | 2319 |
| File Size | 9641686 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781424469840 |
| ISSN | 10636919 |
| e-ISBN | 9781424469857 |
| DOI | 10.1109/CVPR.2010.5539917 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Layout Histograms Robustness Computational complexity Testing Frequency Earth Laboratories Pattern recognition Automation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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